Hanan M. Alghamdi

Scopus Publications

Proactive healthcare: machine learning-driven insights into kidney failure prediction
Hanan Alghamdi
Journal of Umm Al Qura University for Engineering and Architecture, 2025
Kidney failure, a critical condition with increasing prevalence, necessitates early detection and management to mitigate its severe health impacts. In this study, we utilize the MIMIC-IV dataset to develop predictive models for identifying and forecasting kidney failure using advanced machine learning techniques. We aggregated medical records from patients diagnosed with kidney failure, alongside an equivalent dataset from non-kidney failure individuals, to train LSTM, random forest, and XGBoost models. Comprehensive data analysis was conducted to extract and evaluate key features related to kidney function, including correlations among lab events, prescriptions, and patient demographics. These insights informed the model development, enabling accurate classification of kidney failure based on historical medical data and prediction of its onset through time-series analysis. The Random Forest and XGBoost models outperformed others, achieving near-perfect accuracy, demonstrating their robustness in handling complex medical datasets. Additionally, we conducted feature prediction, forecasting critical lab event values for patients with kidney failure, which can inform early interventions and personalized treatment plans. Our findings underscore the potential of machine learning in enhancing clinical decision-making, offering a pathway to more precise and proactive healthcare strategies in managing kidney failure.
Advancing EHR analysis: Predictive medication modeling using LLMs
Hanan Alghamdi, Abeer Mostafa
Information Systems, 2025
In modern healthcare systems, the analysis of Electronic Health Records (EHR) is fundamental for uncovering patient health trends and enhancing clinical practices. This study aims to advance EHR analysis by developing predictive models for prescribed medication prediction using the MIMIC-IV dataset. We address data preparation challenges through comprehensive cleaning and feature selection, transforming structured patient health data into coherent sentences suitable for natural language processing (NLP). We fine-tuned several state-of-the-art large language models (LLMs), including Llama2, Llama3, Gemma, GPT-3.5 Turbo, Meditron, Claude 3.5-Sonnet, DeepSeek-R1, Falcon and Mistral, using tailored prompts derived from EHR data. The models were rigorously evaluated based on Cosine similarity, recall, precision, and F1-score, with BERTScore as the evaluation metric to address limitations of traditional n-gram-based metrics. BERTScore utilizes contextualized token embeddings for semantic similarity, providing a more accurate and human-aligned evaluation. Our findings demonstrate that the integration of advanced NLP techniques with detailed EHR data significantly improves medication management predictions. This research highlights the potential of LLMs in clinical settings and underscores the importance of seamless integration with EHR systems to improve patient safety and healthcare delivery.
Towards Reliable Healthcare LLM Agents: A Case Study for Pilgrims during Hajj
Hanan M. Alghamdi, Abeer Mostafa
Information Switzerland, 2024
There is a pressing need for healthcare conversational agents with domain-specific expertise to ensure the provision of accurate and reliable information tailored to specific medical contexts. Moreover, there is a notable gap in research ensuring the credibility and trustworthiness of the information provided by these healthcare agents, particularly in critical scenarios such as medical emergencies. Pilgrims come from diverse cultural and linguistic backgrounds, often facing difficulties in accessing medical advice and information. Establishing an AI-powered multilingual chatbot can bridge this gap by providing readily available medical guidance and support, contributing to the well-being and safety of pilgrims. In this paper, we present a comprehensive methodology aimed at enhancing the reliability and efficacy of healthcare conversational agents, with a specific focus on addressing the needs of Hajj pilgrims. Our approach leverages domain-specific fine-tuning techniques on a large language model, alongside synthetic data augmentation strategies, to optimize performance in delivering contextually relevant healthcare information by introducing the HajjHealthQA dataset. Additionally, we employ a retrieval-augmented generation (RAG) module as a crucial component to validate uncertain generated responses, which improves model performance by 5%. Moreover, we train a secondary AI agent on a well-known health fact-checking dataset and use it to validate medical information in the generated responses. Our approach significantly elevates the chatbot’s accuracy, demonstrating its adaptability to a wide range of pilgrim queries. We evaluate the chatbot’s performance using quantitative and qualitative metrics, highlighting its proficiency in generating accurate responses and achieve competitive results compared to state-of-the-art models, in addition to mitigating the risk of misinformation and providing users with trustworthy health information.
Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models
Hanan M. Alghamdi
Big Data and Cognitive Computing, 2024
Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.
Techniques to detect terrorists/extremists on the dark web: a review
Hanan Alghamdi, Ali Selamat
Data Technologies and Applications, 2022
PurposeWith the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.Design/methodology/approachThis study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.FindingsBased on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.Originality/valueAt the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.
Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification
Yousef Asiri, Hanan T. Halawani, Hanan M. Alghamdi, Saadia Hassan Abdalaha Hamza, Sayed Abdel-Khalek, Romany F. Mansour
Applied Sciences Switzerland, 2022
Hate speech has become a hot research topic in the area of natural language processing (NLP) due to the tremendous increase in the usage of social media platforms like Instagram, Twitter, Facebook, etc. The facelessness and flexibility provided through the Internet have made it easier for people to interact aggressively. Furthermore, the massive quantity of increasing hate speech on social media with heterogeneous sources makes it a challenging task. With this motivation, this study presents an Enhanced Seagull Optimization with Natural Language Processing Based Hate Speech Detection and Classification (ESGONLP-HSC) model. The major intention of the presented ESGONLP-HSC model is to identify and classify the occurrence of hate speech on social media websites. To accomplish this, the presented ESGONLP-HSC model involves data pre-processing at several stages, such as tokenization, vectorization, etc. Additionally, the Glove technique is applied for the feature extraction process. In addition, an attention-based bidirectional long short-term memory (ABLSTM) model is utilized for the classification of social media text into three classes such as neutral, offensive, and hate language. Moreover, the ESGO algorithm is utilized as a hyperparameter optimizer to adjust the hyperparameters related to the ABLSTM model, which shows the novelty of the work. The experimental validation of the ESGONLP-HSC model is carried out, and the results are examined under diverse aspects. The experimentation outcomes reported the promising performance of the ESGONLP-HSC model over recent state of art approaches.
Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media
Hanan M. Alghamdi, Saadia H.A. Hamza, Aisha M. Mashraqi, Sayed Abdel-Khalek
Computers Materials and Continua, 2022
World Wide Web enables its users to connect among themselves through social networks, forums, review sites, and blogs and these interactions produce huge volumes of data in various forms such as emotions, sentiments, views, etc. Sentiment Analysis (SA) is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive, negative, and neutral. However, Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing (NLP). Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications. So, there is a need exists to develop a proper technique for both identification and classification of sentiments in social media. To get rid of these problems, Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability. The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification (SOADL-SAC) for social media. The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media. In order to attain this, SOADL-SAC model carries out data preprocessing to clean the input data. In addition, Glove technique is applied to generate the feature vectors. Moreover, Self-Head Multi-Attention based Gated Recurrent Unit (SHMA-GRU) model is exploited to recognize and classify the sentiments. Finally, Seeker Optimization Algorithm (SOA) is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results. In order to validate the enhanced outcomes of the proposed SOADL-SAC model, various experiments were conducted on benchmark datasets. The experimental results inferred the better performance of SOADL-SAC model over recent state-of-the-art approaches.
Arabic Web page clustering: A review
Hanan M. Alghamdi, Ali Selamat
Journal of King Saud University Computer and Information Sciences, 2019
Clustering is the method employed to group Web pages containing related information into clusters, which facilitates the allocation of relevant information. Clustering performance is mostly dependent on the text features' characteristics. The Arabic language has a complex morphology and is highly inflected. Thus, selecting appropriate features affects clustering performance positively. Many studies have addressed the clustering problem in Web pages with Arabic content. There are three main challenges in applying text clustering to Arabic Web page content. The first challenge concerns difficulty with identifying significant term features to represent original content by considering the hidden knowledge. The second challenge is related to reducing data dimensionality without losing essential information. The third challenge regards how to design a suitable model for clustering Arabic text that is capable of improving clustering performance. This paper presents an overview of existing Arabic Web page clustering methods, with the goals of clarifying existing problems and examining feature selection and reduction techniques for solving clustering difficulties. In line with the objectives and scope of this study, the present research is a joint effort to improve feature selection and vectorization frameworks in order to enhance current text analysis techniques that can be applied to Arabic Web pages. Keywords: Feature selection, Feature reduction, K-means, Review, Text clustering, ARABIC Web page
Topic Modelling Used to Improve Arabic Web Pages Clustering
Hanan Alghamdi, Ali Selamat
2015 International Conference on Cloud Computing Iccc 2015, 2015
Topic modelling main purpose is to have machine-understandable and semantic annotation to textual contents of Web.It aim to extract knowledge rather than unrelated information. In this paper, we evaluate the impact of using topic model (which intended to represent the documents like a combination of topics where each topic is a mix of vectors) in improving documents clustering results. We have compared the results of clustering using PLSA or LSA. The experiments performed on a set of common newspaper websites that have highly dimensional data and we use Purity, Mean intra-cluster distance (MICD) and Davies-Bouldin index (DBI) for clustering evaluation. Thus, we acquired favorable clustering results, especially in the context of the Arabic language as PLSA were effective in minimizing MICD, expanding purity and bringing down DBI.
Improved Text Clustering Using k-Mean Bayesian Vectoriser
Hanan M. Alghamdi, Ali Selamat, Nor Shahriza Abdul Karim
Journal of Information and Knowledge Management, 2014
In literature studies, high-dimensional data reduces the efficiency of clustering algorithms and maximises execution time. Therefore, in this paper, we propose an approach called a BV-kmeans (Bayesian Vectorisation along with k-means) that aims to improve document representation models for text clustering. This approach consists of integrating the k-means document clustering with the Bayesian Vectoriser that is used to compute the probability distribution of the documents in the vector space in order to overcome the problems of high-dimensional data and lower the consumption time. We have used various similarity measures which are namely: K divergence, Squared Euclidean distance and Squared χ2 distance in order to determine the effective metrics for modelling the similarity between documents with the proposed approach. We have evaluated the proposed approach on a set of common newspaper websites that have highly dimensional data. Experimental results show that the proposed approach can increase the degree to which a cluster encases documents from a specific category by 85%. This is in comparison with the standard k-means algorithm and it has succeeded in lowering the runtime using the proposed approach by 95% compared to the standard k-means algorithm.
The hybrid feature selection k-means method for Arabic webpage classification
Hanan Alghamdi, Ali Selamat
Jurnal Teknologi, 2014
Arabic web pages clustering and annotation using semantic class features
Hanan M. Alghamdi, Ali Selamat, Nor Shahriza Abdul Karim
Journal of King Saud University Computer and Information Sciences, 2014
Topic detections in Arabic Dark websites using improved Vector Space Model
Hanan M. Alghamdi, Ali Selamat
Conference on Data Mining and Optimization, 2012

Hanan M. Alghamdi

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