Dhilsath Fathima M

@dhilsatm@srmist.edu.in

Assistant Professor
SRM institute of science and technology, Kattankulathur



              

https://researchid.co/dhilsath

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition

17

Scopus Publications

Scopus Publications

  • Enhanced Ensemble Classifiers for Heart Disease Prediction
    M. Dhilsath Fathima, M. Manikandan, M. Seeni Syed Raviyathu Ammal, K. Kiruthika, J. Deepa, and Prashant Kumar Singh
    Springer Nature Singapore

  • Sign Language Interpreter Using Stacked LSTM-GRU
    M. Dhilsath Fathima, R. Hariharan, Sachi Shome, Manbha Kharsyiemlieh, J. Deepa, and K. Jayanthi
    Springer Nature Singapore

  • Multiple Imputation by Chained Equations- K -Nearest Neighbors and Deep Neural Network Architecture for Kidney Disease Prediction
    M. Dhilsath Fathima, R. Hariharan, and S. P. Raja
    World Scientific Pub Co Pte Ltd
    Chronic kidney disease (CKD) is a health concern that affects people all over the world. Kidney dysfunction or impaired kidney functions are the causes of CKD. The machine learning-based prediction models are used to determine the risk level of CKD and assist healthcare practitioners in delaying and preventing the disease’s progression. The researchers proposed many prediction models for determining the CKD risk level. Although these models performed well, their precision is limited since they do not handle missing values in the clinical dataset adequately. The missing values of a clinical dataset can degrade the training outcomes that leads to false predictions. Thus, imputing missing values increases the prediction model performance. This proposed work developed a novel imputation technique by combining Multiple Imputation by Chained Equations and [Formula: see text]-Nearest Neighbors (MICE–KNN) for imputing the missing values. The experimental results show that MICE–KNN accurately predicts the missing values, and the Deep Neural Network (DNN) improves the prediction performance of the CKD model. Various metrics like mean absolute error, accuracy, specificity, Matthews correlation coefficient, the area under the curve, [Formula: see text]-score, sensitivity, and precision have been used to evaluate the proposed CKD model performance. The performance analysis exhibits that MICE–KNN with deep learning outperforms other classifiers. According to our experimental study, the MICE–KNN imputation algorithm with DNN is more appropriate for predicting the kidney disease.

  • Automatic Title Generation with Attention-Based LSTM
    M. Dhilsath Fathima, M. Seeni Syed Raviyathu Ammal, Prashant Kumar Singh, Sachi Shome, Manbha Kharsyienlieh, and R. Hariharan
    Springer Nature Singapore

  • Abstractive Summarization is Improved by Learning Via Semantic Similarity
    R. Hariharan, M. Dhilsath Fathima, A. Kameshwaran, A. Bersika M. C. Sweety, Vaidehi Rahangdale, and Bala Chandra Sekhar Reddy Bhavanam
    Springer Nature Singapore

  • Lifestyle Disease Influencing Attribute Prediction Using Novel Majority Voting Feature Selection
    M. Dhilsath Fathima, Prashant Kumar Singh, M. Seeni Syed Raviyathu Ammal, and R. Hariharan
    Springer Nature Switzerland

  • Handwritten Digit Recognition Using Very Deep Convolutional Neural Network
    M. Dhilsath Fathima, R. Hariharan, and M. Seeni Syed Raviyathu Ammal
    Springer Nature Singapore

  • Majority voting ensembled feature selection and customized deep neural network for the enhanced clinical decision support system
    M. Dhilsath Fathima, S. Justin Samuel, R. Natchadalingam, and V. Vijeya Kaveri
    Informa UK Limited
    Heart disease and diabetes are global health issues that affect people worldwide. Diabetes is becoming a significant concern, and Diabetes patients have a substantially higher risk of heart disease morbidity and mortality than people without diabetes. These conditions are associated with hospitalizations and emergency room visits, which raises healthcare expenses. An important strategy to improve health care outcomes and reduce unnecessary costs is to identify and anticipate them in patients. Clinical Decision Support Systems (CDSS) assess patient data from clinical datasets to help disease prediction and enhance treatment options for heart disease and diabetes, and other disorders. According to the literature, most CDSS have used machine learning algorithms for predicting heart disease and diabetes. These algorithms performed worthily, but the accuracy of these machine learning (ML) algorithms is lacking, especially in medical data, which contains numerous complex attributes such as resting blood pressure, serum cholesterol, fasting blood sugar, and thalassemia value. This proposed work developed a majority voting ensembled feature selection (MVEFS) technique and customized deep neural network (CDNN) to develop a CDSS for heart disease and diabetes prediction. This deep neural network-based CDSS best performing than ML-based CDSS. There are several input attributes in the clinical dataset. Some attributes are not associated with disease and have negative consequences when used in clinical data analysis for disease prediction. As a result, feature selection is essential for removing unimportant features. The feature selection significantly minimizes system learning time, which improves CDSS performance efficacy. The MVEFS selects the associated heart disease and diabetes-related features from the clinical dataset. The classifier execution time, accuracy, sensitivity, precision, specificity, and F1-score are the performance metrics used to evaluate the proposed CDSS. According to our experimental study, the MVEFS with a customized deep neural network is more appropriate for predicting heart and diabetes than machine learning algorithms.



  • Handwritten digits classification through multi-classifier bag of visual words


  • Diagnosis of acute myocardial infarction using random forest classifier through SPECT


  • Analysis of machine learning algorithms for effective prediction of cardiovascular disease
    M. Dhilsath Fathima and S. Justin Samuel
    American Scientific Publishers

  • Privacy preserving multi-party hierarchical clustering over vertically partitioned dataset using semi-honest model


  • K-means clustering algorithm to improve website performance


  • Identification of bacterial contamination: A classification approach using support vector machine



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