Attique Ur Rehman

EDUCATION

MS Software Engineering From NUST

RESEARCH INTERESTS

Ai in Medicine, Applied Machine Learning, Pure Software Engineering,

Scopus Publications

A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques
Azka Mir, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, Maram Fahaad Almufareh, Mamoona Humayun, and Momina Shaheen
Wiley
AbstractAimsThe objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease.MethodsIn this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three‐step approach includes pre‐processing of the dataset, applying feature selection method on pre‐processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10‐fold cross‐validation provided the high accuracy.ResultsThe proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively.ConclusionsIn conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages.


An Intelligent Technique for Predicting Quality of Drinking Water
Intizan Nadeem, Abdullah Yahya, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, and Azka Mir
IEEE
Water is an essential part of our lives, but as time passes water resources are becoming polluted worldwide due to industrial pollution, human waste, and many other factors. It has many hazardous effects on humans and other living beings therefore, it became crucial to monitor water. This research aims to predict water quality based on various factors of drinking water using supervised machine learning algorithms. In the context of predicting water quality, supervised machine-learning strategies are quite important. These algorithms are trained with historical data that includes information on various factors affecting water quality. This work uses machine learning algorithms to determine drinking water quality using a public dataset. Four distinct algorithms for machine learning Random Forest, Decision Tree, K-Nearest Neighbors, and Naive Bayes are used to predict the water quality. The performance of each algorithm is evaluated and compared based on their accuracy rates. After applying algorithms, the combination of two algorithms Random Forest and Decision Tree achieved perfect accuracy of 99.68%. The outcomes of the proposed model may contribute to effective water quality monitoring.


Type 2 Diabetes Mellitus Monitoring Through Non-invasive IoT-Based System
Aamir Hussain, Attique ur Rehman, Altaf Hussain, Qimeng Li, Raffaele Gravina, and Giancarlo Fortino
Springer Nature Switzerland



An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction
Hassan Ali, Hussain Imtiaz, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Menwa Alshammeri, Azka Mir, and Himanshu Kumar
IEEE
Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. This increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. This research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. The insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease.


Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques
Ahmed Baber, Faizan Ahmed, Attique Ur Rehman, Sabeen Javaid, Menwa Alshammeri, Azka Mir, and Deepak Kumar
IEEE
Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. The heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. The fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. This study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. This research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. The results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies.


The Future of Differentiated Thyroid Cancer Recurrence Prediction Using a Machine Learning Framework Advancements, Challenges, and Prospects
Irsa Imtiaz, Attique Ur Rehman, Sabeen Javaid, Tahir Mohammad Ali, Azka Mir, Mehedi Masud, and Yadaiah Nirsanametla
IEEE
Differentiated thyroid cancer originates in the thyroid gland, which is positioned in the front of the neck. The thyroid gland generates hormones that control metabolism, heart rate, and other bodily functions. Differentiated thyroid carcinoma (DTC) recurrence is a major challenge in clinical therapy. Early detection and treatment play an important role in reducing the impact of thyroid cancer recurrence. The development of precise prediction algorithms is demanding. The prognosis, diagnosis, and treatment of differentiated thyroid carcinoma have been the subject of extensive investigation. Scholars have investigated diverse methodologies to forecast the likelihood of getting thyroid cancer, refine early identification techniques, and augment therapeutic results. Machine learning (ML) frameworks have emerged as useful tools in this setting, with the potential to improve prediction accuracy and patient outcomes. This paper provides a detailed evaluation of the current state and future directions of DTC recurrence prediction using machine learning. We examine current advances in machine learning techniques, data sources, and feature selection approaches used in DTC recurrence prediction models. Several machine-learning algorithms have been applied. We have suggested the model containing the classifier with the highest accuracy after comparing the accuracy percentage of the various classifiers that were produced. Our suggested model has a 98.17% accuracy rate with Bagging. Finally, we propose solutions to these difficulties and emphasize ML's potential to revolutionize the landscape of DTC recurrence prediction. We hope that this analysis will provide insights into the developing role of machine learning in DTC management and motivate more research in this crucial area.


An Intelligent Technique for the Effective Prediction of Parkinson Disease
Sawera Tariq, Madiha Qadeer, Attique Ur Rehman, Sabeen Javed, Tahir Muhammad Ali, Azka Mir, and Suresh Singh
IEEE
Parkinson is a condition of the nervous system that is linked to breakdown of basal ganglia of the brain. This disorder is responsible for affecting the neurological system and other body parts that are nerve-controlled. Its symptoms vary from person to person, however common symptoms include Tremor, Slow Movement and Stiffness in Muscles, Impaired Posture, Speech Changes and Loss of Automatic Movements. The main cause of this disease is not known, that is why it is not curable and has no proven prevention yet. This research concerns the application of machine learning classifiers including KNN, Logistic Regression, Decision Tree, Naive Bayes and Random Forest for the detection of Parkinson's disease. We are using a Parkinson disease dataset using a machine learning algorithm for detection to determine the performance in terms of accuracy. In a nut shell, the Random Forest classifier, without using SMOTE, provided the highest accuracy among the models considered. However, this result and existing results as well are based on imbalanced class. This research is contributing valuably for the convergence of machine learning and healthcare for effective predictions of this disease using Smote for balancing out same dataset classes with accuracy of 98.7%. The results hold significance for future modifications in medical diagnostics for more accurate and effective detection methodologies.


The Sophisticated Prognostication of Migraine Aura Using Machine Learning
Samiullah, Abdul Rehman, Attique Ur Rehman, Sabeen Javaid, Tahir Mohammad Ali, Azka Mir, and Yadaiah Nirsanametla
IEEE
Migraine is one of the most disabling diseases in the world and impacting more than one billion individuals. The symptoms such as intensity, Nausea, Vomit, Phonophobia, Photophobia, Visual, Dysphasia, Dysarthria, Vertigo, Sensory, intense to sound, occurs before the migraine. Migraine drains the quality of persons' life. Furthermore, the study of this research was to train machine learning model on migraine aura dataset using different modern approaches that could assist medical patients before occurring migraine and could also give the indications related to migraine. Likewise, by applying random forest algorithm, we got 99.5 percent accuracy using google colab which will be enough to deploy on application for future Project. Moreover, we leveraged traditional framework to complete the research incorporating, Data collection (Online free Kaggle), Preprocessing, Classification, Model training and Comparison of models. As a result, our peak algorithm precision was including, Naive Bayes 94.52%, decision tree 98.3%, K-nearest neighbours 98.6%, random forest 99.5%, Similarly, Rapid miner and Google Colab software are used for the comparison of algorithms and best one is chosen.


Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis Through Machine Learning
Isra Imtiaz, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir, Mehedi Masud, and Deepak Kumar
IEEE
A significant risk factor for the development of liver disease is hemochromatosis, an inherited iron overload illness, but the complex pathophysiology behind this link is still unknown. To understand the intricate interactions between hemochromatosis and liver disease, this study uses a cutting-edge strategy that incorporates machine learning techniques. Promising prediction skills are demonstrated by machine learning models, which open up new possibilities for early detection and individualized intervention approaches. The results shed important light on the relationship between hemochromatosis and liver disease and improve our understanding of the pathogenesis of the disease. This study clarifies the pathophysiology of hemochromatosis and its subsequent connection to liver disease, highlighting the revolutionary power of machine learning in understanding intricate biological processes. This selected dataset contains 15 parameters and has 202 dated records with a target value of ‘Disease Name’ which has 5 different categories Type 1, 2, 2A, 2B,2C, 3, 4, and Type5. The experiment is performed using two classifiers including KNN and Naïve Bayes. The k-fold cross-validation technique is also used to improve the performance. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 98.00% using a K-NN classifier. Rapid miner platform is used for the application of the machine learning tools and techniques for this research These discoveries have consequences that go beyond clarifying the mechanisms behind disease; they also open the door to better clinical care and tailored treatment therapies for high-risk people.


Beyond Glucose Levels: A Machine Learning Perspective on Type 2 Diabetes Prediction
Amara Aslam, Arooj Ashraf, Attique Ur Rehman, Sabeen Javaid, A A Khan, Azka Mir, and Deepak Kumar
IEEE
Type 2 diabetes hinders the body from using insulin effectively. The primary source of fuel for the human body is sugar. However, individuals with type 2 diabetes are unable to utilize it effectively due to insufficient insulin production by the pancreas. This medical condition is responsible for numerous fatalities annually. While it was previously thought to only be found in adults, it is now being diagnosed in children. This study aims to predict this disease before it becomes severe. There are 18 unique features in the dataset, which was collected in 2016 by Chinese researchers under WHO standards. The dataset contains 1304 patient samples and over 4000 rows of data. Different supervised machine-learning algorithms, including KNN, Decision Tree, Random Forest, Naïve Bayes, and Logistic Regression, are used to determine the dataset's performance. The best accuracy of 95.12% was achieved using Random Forest without SMOTE. Rapid Miner was used to test and train the data during the study. This paper outlines the effective machine learning processes for designing a suitable and accurate model for early-stage type 2 diabetes prediction.


Predictive Modeling of Students' Stress Levels Using Machine Learning Algorithm
Hassan Ali, M. Hamza Amin, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, and Azka Mir
IEEE
The increasing prevalence of stress among university students has raised concern about its impact on academic performance and overall well-being. This conference paper explores the applications of machine learning algorithms to predict and analyze the student's stress level. A comprehensive dataset encompassing various stress-related factors, including academic workload, social interactions, and lifestyle, was collected from Kaggle which consists of 1100 instances and 19 attributes. Our methodology places a premium on human connection, integration of qualitative data to refine the accuracy of stress predictions. By seamlessly merging the capability of machine learning with an empathetic understanding of the human experience, our approach strives to pave the way for a more holistic and personalized educational ecosystem. The insights derived from this study aspire to guide educators, administrators, and policy makers in crafting nurturing environments that empower students to excel both academically and emotionally. Embarking on a journey to unravel the myriad factors influencing students' stress, we consider academic pressures, social dynamics, and personal experiences. Harnessing machine learning algorithms such as decision trees, neural networks, we navigate expansive student populations. Acknowledging the nuanced nature of stress, our study integrates a compassionate approach with cutting-edge technology. The study employs state-of-the-art machine learning techniques using various classification algorithms, to build predictive models for assessing stress levels out of which random forest gives 97.45% accuracy.


An Integrated Machine Learning Framework Based Liver Disease Diagnosis System
Irsa Imtiaz, Ayesha Qaiser, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, and Azka Mir
IEEE
Viral hepatitis increases the likelihood of developing liver cancer, which is a critical public health concern. It is a severe disorder that can be fatal if not recognized and treated promptly. The most common causes of liver cancer are hepatitis viruses. Early-stage diagnosis of HCC is crucial for improving patient outcomes, particularly when hepatitis virus infection is the underlying cause. This study looks into the use of ML algorithms for early detection of liver cancer in hepatitis virus-infected patients. The study will present a unified approach for the early detection of liver cancer that is highly accurate. Early detection of liver cancer is critical in reducing mortality and assisting practitioners in treating patients on time based on results. Machine learning algorithms have been intensively researched and applied to the diagnosis of liver cancer, especially in recent years. Researchers employed a variety of machine learning techniques to create models for detecting liver cancer at different stages, including early diagnosis. To enhance the model's flexibility and efficiency, We used three datasets that contained all of the information required for diagnosing liver cancer. Several machine-learning approaches were used to improve the model's accuracy. Our proposed model acquired a 98.10% accuracy rate with Bagging. Using several datasets in machine learning for liver disease detection enhances model performance by diversifying the training examples, resulting in a more robust and generalizable model with lower overfitting risk. This study aims to improve the quality of life for people who are at risk of or have been diagnosed with liver cancer.


A Comprehensive Prediction Model for T20 and Test Match Outcomes Using Machine Learning
Zoha Ahsan, Shahwaiz Ghumman, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, and Azka Mir
IEEE
Cricket is a popular sport worldwide, played with a bat and balls. This paper used classification and regression to predict the T20 and Test matches results and scores because cricket fans and analysts always want to predict which team will win the match and how much score a team will make. We have used a supervised machine learning technique to predict the score and result for T20 and Test matches. Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model. For the prediction of winning and losing, we have used Naïve Bayes, Gradient Boosted Trees, Logistic Regression, Deep learning, Decision Tree, Random Forest, and Generalized Linear Model. For Score Prediction, we have used Decision Trees, Random Forests, Gradient Boosted Trees, Generalized Linear Models, and Deep Learning. We've shown the most accurate model after evaluating the accuracy percentages of the several classifiers listed above. Our introduced model has gained an accuracy rate of 96.15% using the Gradient Boosted Trees classifier for Match Score Prediction and 71.72% accuracy by using the Generalized Linear Model for match result prediction. The Rapid Miner tool has been used to perform machine learning techniques to train models. This paper discusses the effectiveness and use of machine learning methods to develop highly accurate models for Cricket match prediction dataset comprising of 13 features and 7827 instances utilized for training the model.


Enhancing Brain Stroke Risk Prediction with Multi-Algorithm Evaluation and Web Interface
Muhammad Ahmed, Umer Liaquat, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, and Azka Mir
IEEE
Brain stroke is the world's leading cause of death, impacting numerous lives annually. The chances of having a stroke have increased by 50% over one's lifetime, impacting one in four people worldwide. Machine learning may assist in the prior detection of strokes, allowing for more timely diagnosis which can save a person's life and disability. However, existing methods face challenges in integrating ML models with clinical applications to predict patient stroke status with optimal accuracy. This study conducts a comprehensive investigation into brain stroke risk prediction using numerous ML algorithms, including Random Forest, Logistic Regression, KNN, Naive Bayes, XGBoost, and LightGBM. Our main contribution is the development of a web app that predicts stroke based on user inputs with optimal accuracy. The selected dataset contains 5110 records of normal people and 249 records of people having strokes which is highly imbalanced. To ensure model robustness, the dataset undergoes balancing through SMOTE oversampling and KNN Imputation for handling missing values. Custom thresholds for classifiers optimize training, with comprehensive evaluations utilizing key statistics such as confusion matrices, F1-Score, recall, accuracy, and precision. Cross-validation is employed to assess potential overfitting and underfitting risks in machine learning models. Random Forest ranked as the most imposing, reaching a remarkable 99.85% accuracy on the test set with a custom threshold of 0.6 and a random state at 42. Additionally, a user-friendly Web App has been developed to predict stroke status based on user input, enhancing practical usability and accessibility for individuals seeking risk assessments.


Exploring Sleep Paralysis Phenomenon Through Machine Learning: An Analytical Study
Samra Ishaq, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, and Azka Mir
IEEE
Sleep paralysis is when you're awake but powerless to move. Although the majority of occurrences are linked to extreme terror and some potentially clinically significant sufferings are connected with the case; little is known about it. The present state of research on the connection between sleep paralysis and overall sleep will be examined in this study. Several studies have linked an increased risk of sleep paralysis to low-quality sleep.. This could occur in between sleeping and waking. The issue is tackled in the context of sleep paralysis prediction using machine learning. There are multiple attributes in the dataset (label Data). Every exam has a unique set of characteristics and expected results. We carried out an accuracy test of posture recognition by the system to evaluate its validity. Predicting sleep paralysis has been done using machine learning techniques. A comparison of sleeping positions with the findings indicated a correlation between paralysis and poor quality of sleep. Poor sleep quality is correlated with sleep paralysis. In the provided dataset, the Random Forest model predicts sleep paralysis with the highest accuracy 92.3%, K-nearest neighbors' classifier has an accuracy rate of 91.0% 91.9%, and 90.4% using Decision Tree and naïve Bayes.


Leveraging Ensemble Learning for Dry Beans Classification
Muhammad Jahanzaib, Qasim Zaheer, Attique Ur Rehman, Sabeen Javed, Tahir Muhammad Ali, and Azka Mir
IEEE
For dietary and commercial purposes, dry beans are significant. Dry beans offer many advantages, such as energy and protein. It is crucial to the fight against malnutrition and ensuring food security. The most crucial element in the manufacturing of dry beans is the condition of the seed. As the quality of the seed is so important, seed verification is crucial for dry bean farmers and marketplaces. Choosing high-quality seeds is essential to achieving optimal yield. This research uses a variety of machine-learning techniques to classify different varieties of dry beans. For this research work, a dataset from the UCI machine learning repository is used. The outcomes of model evaluation have been compared, and various classifiers have been trained. In the process, the k-fold cross-validation technique is applied with k = 10 as a parameter. This research aims to differentiate between the seven different types of dry beans by using classification algorithms. An accuracy of 82.01%, 93.46%, 94.67%, and 94.64% has been achieved with the k-nearest neighbor, decision tree, naïve Bayes, and random forest classifiers respectively. Also, the voting ensemble classifier has an accuracy of 95.46%. After the experiments, it was observed that the voting ensemble classifier has the highest accuracy. Future research can examine the performance of new algorithms for better results. Additionally, the dataset can be increased to improve the model's accuracy.


A Machine Learning-Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and Algorithmic Performance
Attique Ur Rehman, Wasi Haider Butt, Tahir Muhammad Ali, Sabeen Javaid, Maram Fahaad Almufareh, Mamoona Humayun, Hameedur Rahman, Azka Mir, and Momina Shaheen
Wiley
The liver is the largest organ of the human body with more than 500 vital functions. In recent decades, a large number of liver patients have been reported with diseases such as cirrhosis, fibrosis, or other liver disorders. There is a need for effective, early, and accurate identification of individuals suffering from such disease so that the person may recover before the disease spreads and becomes fatal. For this, applications of machine learning are playing a significant role. Despite the advancements, existing systems remain inconsistent in performance due to limited feature selection and data imbalance. In this article, we reviewed 58 articles extracted from 5 different electronic repositories published from January 2015 to 2023. After a systematic and protocol‐based review, we answered 6 research questions about machine learning algorithms. The identification of effective feature selection techniques, data imbalance management techniques, accurate machine learning algorithms, a list of available data sets with their URLs and characteristics, and feature importance based on usage has been identified for diagnosing liver disease. The reason to select this research question is, in any machine learning framework, the role of dimensionality reduction, data imbalance management, machine learning algorithm with its accuracy, and data itself is very significant. Based on the conducted review, a framework, machine learning‐based liver disease diagnosis (MaLLiDD), has been proposed and validated using three datasets. The proposed framework classified liver disorders with 99.56%, 76.56%, and 76.11% accuracy. In conclusion, this article addressed six research questions by identifying effective feature selection techniques, data imbalance management techniques, algorithms, datasets, and feature importance based on usage. It also demonstrated a high accuracy with the framework for early diagnosis, marking a significant advancement.


An Applied Artificial Intelligence Aided Technique for Effective Classification of Breast Cancer
Mishal Waqar, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, and Ali Nawaz
IEEE
Among Women, Breast cancer is one of the maximum occurring diseases. Many women die every year because of breast cancer globally. Early prediction and diagnosis of this disease can prevent death in the end. The survival rate increases on detecting breast cancer early as better treatment can be provided. Development in prediction and diagnosis is necessary for the life of people. A higher amount of accuracy in the prediction of breast cancer is necessary for the treatment aspects and also for the survivability of patients. It is apparent that there are different techniques available in breast cancer detection but machine learning algorithms can bring a large contribution to the process of prediction and early diagnosis of breast cancer. In this study, we use a Wisconsin dataset which was collected from a scientific dataset of 569 breast cancer. Out of 569 patients, 63% were diagnosed with benign and 37% were diagnosed with malignant cancer. The benign tumor grows slowly and does not spread. We apply five machine learning algorithms to this dataset and train a model for predicting malignant and benign tissues (BCs). Algorithms are K-Nearest neighbor, Support vector machine, Decision tree, Deep learning, and Random-forest respectively. The effectiveness of these algorithms is evaluated in terms of accuracy, F measure, confusion matrix, and specificity. By comparing the results deep learning classifier gives the highest accuracy and outclass all the other classifiers by attaining an accuracy of 9S.l3%. SVM gives an accuracy of 97.66% whereas KNN gives an accuracy of 95.79% etc.


An Integrated Machine Learning Framework for Effective Classification of Water
Isha Aleem, Attique Ur Rehman, Sabeen Javaid, and Tahir Muhammad Ali
IEEE
Water is an essential need for all living things. The problem people are facing especially in urban areas is the diseases that have been caused by water which is mainly Dengue or malaria because there are many metals such as ammonium aluminum silver and other bacterial or viral things present in water. The first step toward a healthy lifestyle is to drink purified water. In this paper, the methodology that has been used here is to detect whether the water people are using for drinking purposes is safe enough to use or not in the data set and the specific methodology is binomial type as yes or no, 912 for positive states and 7084 negative states which means that 0.8864 for 0 and 0.114 for 1. The ratio for negative is far higher than the positive one. We tested the model in two ways first with simple feature extraction, smote Upsampling and with vote ensemble. In smote Upsampling accuracy with the random forest is 88.21 % (highest). The classification error of random forest is 11.7% and with the highest rate is 94.73% which is recall and specificity is 81.71% which is the lowest in the random forest whereas with vote ensemble the combinations of two algorithms have been used and the highest accuracy is from naive Bayes and KNN and the accuracy from them is 96.00% with the classification error of 6% only its precision rate is higher which is 98.89% and lowest specificity of 75.00%.


An Intelligent Technique for the Effective Prediction of Monkeypox Outbreak
Azka Mir, Attique Ur Rehman, Sabeen Javaid, and Tahir Muhammad Ali
IEEE
Monkey pox is a viral disease that spreads from animals especially monkey to human beings. Monkey pox outbreak has been increasing at a concerning rate. The outbreak of monkey pox has infected several people around the world. The extent and intensity of the disease can be determined by the occurrence of the symptoms. The objective of this paper is to predict monkeypox virus so that outbreak can be administered before monkeypox looms as a viral health hazard. The monkeypox case has been classified as confirmed, discarded and suspected. This paper uses a supervised machine learning model to predict the status of monkey pox case. To diagnose monkeypox virus case, clinical parameters are required. The selected dataset contains the parameters of monkey pox virus from April 2022 onwards. It is necessary to predict the monkey pox outbreak before it effects more valuable lives. For the purpose of this paper, supervised machine learning techniques have been used to determine the performance of the dataset through experimental analysis. The experiment has been performed using various classifiers such as Decision tree, Naïve Bayes etc. to compare the accuracy rate. After the comparative analysis of the resulting accuracy percentage of different classifiers, we have proposed the model with the classifier with the highest accuracy. Our proposed model has achieved an accuracy rate of 93.51% using K-NN classifier with k=5 neighbors. Rapid miner platform is used for the application of the machine learning tools and techniques for the purpose of this research. This paper highlights the effective machine learning steps for the development of highly accurate model using machine learning techniques on monkey pox outbreak dataset.


A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor
Tahir Mohammad Ali, Ali Nawaz, Attique Ur Rehman, Rana Zeeshan Ahmad, Abdul Rehman Javed, Thippa Reddy Gadekallu, Chin-Ling Chen, and Chih-Ming Wu
Frontiers Media SA
Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS’20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.


An Integrated Machine Learning Framework for Classification of Cirrhosis, Fibrosis, and Hepatitis
Sibgha Islam, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, and Ali Nawaz
IEEE
Hepatitis C is an ailment that causes inflammation of the liver and leads to serious liver damage. In previous research, the accuracy of the model wasn't that accurate but the differences this paper made model worked well for the prediction of Hepatitis C disease. In the dataset, there are mainly four categories (Blood Donor, Suspected Blood Donor, Fibrosis, and Cirrhosis) used that are labeled. Its data type is polynomial with 0 missing values. The minimum value in the category is 7 for suspect blood donors and the most value is 533 for a blood donor. The machine learning algorithms used in medical approaches are increasing day by day for prediction tools, diagnosis tools, and detection of diseases such as the hepatitis C virus. We used the rapid miner Software for the application of machine learning algorithms. Firstly, took the dataset of the hepatitis C virus from the UCI machine learning site and then applied the five Machine Learning Algorithms, which include Naive Bayes, Random Forest, KNN, Decision Tree & Deep Learning (ANN). On applying feature selection, the attributes Age, ALB, ALP, AST, CHE, GGT, and PROT were selected. After applying different algorithms, the best results are shown by deep learning (ANN) with an accuracy of 95.50%. Rest all algorithms showed minimum accuracy as a Decision tree with 93.09%, Naïve Bayes with 91.89%, KNN with 93.09%, and Random Forest showed 94.29% of high accuracy.


A Computer Aided Technique for Classification of Patients with Diabetes
Faiza Mehreen, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, and Ali Nawaz
IEEE
Diabetes is a chronic disease that occurs when the sugar level is too high in the body or when the body doesn't make enough insulin and it impacts each individual of all age groups. It has a captivating history that has increased significantly in recent years as a result of urbanization and affected millions of people worldwide. Undiagnosed diabetes can cause many life-threatening diseases which usually lead to the death of a person. So, the early detection of diabetes is very vital to maintain a healthy life and it can help to prevent complications and reduce patients' health risks. This paper undertakes to design a model which gives maximum accuracy by using different machine learning algorithms that help detect the disease in its early stage. For this purpose, used five classifiers which are Random Forest, Decision Tree, K-Nearest Neighbor, Naïve Bayes, and Deep learning, then apply the Vote ensemble approach that is considered “best practice” and is a part of the workflow and provides the best possible outcomes with the highest accuracy percentage. The informational data employed as a part of this analysis is taken from the Kaggle dataset of Early Diabetes Classification and preprocessed this all data on the RapidMiner Tool. The main point of this research is the implementation of the different ML based classification models to show their comparative analysis. Thus, by using these algorithms the diagnosis of diabetes is statistically evaluated and compared. The experimental outcomes show that in the vote ensemble, Random Forest with K-NN gives optimum results with the highest accuracy of 97.97% along with parameters like precision, f-measure, and sensitivity.


A Novel Model-Driven Approach for Visual Impaired People Assistance OPTIC ALLY
Laiba Rana, Attique Ur Rehman, Sabeen Javaid, and Tahir Muhammad Ali
IEEE
People having visual impairment can't perform routine tasks on their own such that they are obliged to perform even the simplest task with assistance. Since their issue can't be resolved with visual glasses or lenses, the various mode of advancing technologies is playing an immense role in aiding them. Plenty of research work and development of gadgets have been proposed regarding the assistance of visually impaired people. From deep learning along with sensor-based initiative systems to various ranges of software have been developed but until now every system or software heeds on a particular feature. This article is based on purposing the idea of developing a cross-platform application named Optic Ally, intelligent assistance for visually impaired people that will provide all salient features in it regarding their assistance. For this idea of Optic Ally application, a novel model-driven framework is introduced that comprises a meta-model, tree editor, and graphical modeling tool that is Sirius based with drag and drop palette. The modeling and visualization of the features of this app are done using the Sirius-based graphical modeling tool. Moreover, a case study has been demonstrated for the validity of the proposed framework. The outcome of the case study proves that the proposed framework is competent in modeling and visualizing features effectively. The initial developed stage of the application is also shown in this article. Optic Ally application features include object detection, object finder, location, voice assistance, guardian help, link account and profile setup for the visually impaired user whereas for the guardian user the features include location, profile setup and guardian help


An Applied Artificial Intelligence Technique For Early Prediction of Diabetes Disease
Abdul Saboor, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, and Ali Nawaz
IEEE
Diabetes Mellitus is a common issue all over the world. There are many cases of diabetes that are recorded on daily bases. This chronic disease takes the entire life of a patient to recover from his health. The doctor can save it only in the early stages of the disease because if it is the last stage, it would be almost impossible for patients to recover from diabetes. The system is proposed to provide the best solution for the early prediction of diabetes. So that the doctor detects the disease in an early stage. The main purpose of research on early diabetes is to go ahead and improve the detection system of diabetes in the model for doctors to predict the patient's disease efficiently. Many research papers are public about different types to predict in different stages. But this work aims to predict the symptom in early-stage to stop the disease from the root so that it would not cause harm in the future. If the problem is solved at the early stage of the disease, it will be easy for patients to recover easily saving them from a big loss in the future. This research applied severer Machine Learning and Deep Learning algorithm to check the model's performance. In return, K Nearest Neighbor and Decision Tree provide the best accuracy in the applied dataset. The highest accuracy KNN model got 93.66% and the Decision Tree got 90.10% accuracy. KNN is the best algorithm in this case which provides good accuracy for the provided dataset. The Dataset used in this research was built by a Bangladesh hospital with a direct survey form with different patients. It has 17 attributes and a collection of 520 instances. The KNN algorithm is handy and has got 93.66% accuracy to apply the medical professional treatment.

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