Machine Learning and Feature Selection-Enabled Optimized Technique for Heart Disease Classification and Prediction

P. Nancy; Prasad Raghunath Mutkule; Kalpana Sunil Thakre; Ajay S. Ladkat; S.B.G. Tilak Babu; Sunil L. Bangare; Mohd Naved

doi:10.24423/cames.2024.602

P. Nancy Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattangulathur Campus, Chennai, India
Prasad Raghunath Mutkule Department of Information Technology, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
Kalpana Sunil Thakre Department of Computer Engineering, MMCOE, Savitribai Phule Pune University, Pune, India
Ajay S. Ladkat Department of Instrumentation Engineering, Vishwakarma Institute of Technology, Pune, India
S.B.G. Tilak Babu Aditya Engineering College, Surampalem, India
Sunil L. Bangare Department of Information Technology, Sinhgad Academy of Engineering, Savitribai Phule Pune University, Pune, India
Mohd Naved Jaipuria Institute of Management, Noida, India

DOI: http://dx.doi.org/10.24423/cames.2024.602

Abstract

When dealing with a group of patients seeking treatment for heart-related diseases, doctors who specialize in the diagnosis and treatment of heart-related disorders have a difficult but critical task. It comes as no surprise that cardiovascular disease is a leading source of morbidity and death in contemporary society. An expert system with clear categorization that may assist medical professionals in identifying heart disease condition based on the clinical data of a patient is often required by physicians. The aim of this work is to provide a method for the prediction and classification of cardiac disease based on machine learning and feature selection. The correlation-based feature selection (CFS) method is applied to the input data set in order to extract relevant features for analysis. The support vector machine with radial basis function (SVM RBF) and random forest algorithms are used here for data classification. Cleveland heart disease dataset is used in the experiment work. This dataset has 303 instances and 14 attributes. The accuracy, specificity and sensitivity of SVM RBF are higher than those of the random forest algorithm.

Keywords

machine learning, heart disease prediction, accuracy, SVM RBF, CFS feature selection,

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