Majority voting ensembled feature selection and customized deep neural network for the enhanced clinical decision support system

Abstract

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.

KEYWORDS:

• Customized deep neural network
• majority voting ensembled feature selection
• heart disease prediction
• diabetes prediction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

M. Dhilsath Fathima

M. Dhilsath Fathima received her B.E degree in CSE from Anna University, Tamil Nadu, India in 2005, and a Master's degree in CSE from Sathyabama Institute of Science & Technology, Tamil Nadu, India in 2011. Now, pursuing a Ph.D. in Computer science and Engineering Department from Sathyabama Institute of Science & Technology, Tamil Nadu, India. Since 2007, she is working as an Assistant Professor in Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India. Her major research areas are Machine learning, Data mining, and Deep learning.

S. Justin Samuel

Dr. S. Justin Samuel received his Ph.D. in Computer Science and Engineering from Sathyabama University, India. His area of interest includes Data Mining, Wireless Sensor Networks, and Image processing. He has published more than 25 research papers in International & National Journals and Conferences. He is a professor at PSN Engineering college for the Department of Science and Technology, India.

R. Natchadalingam

Dr. R. Natchadalingam graduated with a Master of Engineering in CSE from Madurai Kamaraj University, a Master of Technology in Networking from KL University, and a Ph.D. from MS University. Initially, his service was in Software Industries and worked in Reputed Engineering Colleges presently he is working as a Professor and Dean of PSN Engineering College, Tamil Nadu. He published many research papers in the area of Networking, Cloud Computing, and other Computer Engineering Applications.

V. Vijeya Kaveri

Dr. V. Vijeya Kaveri completed her Ph.D. in the Faculty of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India in the year 2019. She received her M.Tech. degree in Information Technology from Sathyabama University, Chennai India in the year 2007 and B.E. in Computer Science and Engineering from Madras University, India in the year 1998. Presently she is working as Professor at Sri Krishna College of Engineering and Technology, Coimbatore.