Feature selection and prediction of heart disease using machine learning approaches

Molla, M. M.Imran and Islam, Md Sakirul and Shafi, A. S.M. and Alam, Mohammad Khurshed and Islam, Md Tarequl and Jui, Julakha Jahan (2022) Feature selection and prediction of heart disease using machine learning approaches. In: Lecture Notes in Electrical Engineering; 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021 , 23 August 2021 , Kuantan, Pahang. pp. 951-963., 842 (274719). ISSN 1876-1100 ISBN 978-981168689-4

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Abstract

Heart Disease (HD) is the world's most serious illness that seriously impacts human life. The heart does not push blood to other areas of the body in cardiac disease. For the prevention and treatment of cardiac failure, accurate and timely diagnosis of heart disease is critical. The diagnosis of cardiac disease has been considered via conventional medical history. Non-invasive approaches like machine learning are effective and powerful to categorize healthy people and people with heart disease. In the proposed research, by using the cardiovascular disease dataset, we created a machine-learning model to predict cardiac disease. In this paper, it is capable of recognizing and classifying the heart disease patient from healthy people by using three standard machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In addition, the Area Under Curve (AUC) value is calculated for each classification algorithms. In the proposed scheme, we also used the feature selection algorithm to reduce dimensions over a qualified heart disease dataset. After that, the whole structure for the classification of heart disease has been created. On complete features and reduced features, the performance of the proposed approach has been verified. The decrease in features affects the accuracy and time of execution of the classifiers. With the selected features, the highest classification accuracy is obtained for the KNN algorithm is about 93%, with a sensitivity is 0.9750 and specificity is 0.8529. Therefore, with the complete features, the classification accuracy is about 91%.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Cancer; Heart disease; K-Nearest Neighbor (KNN); Machine learning; Random forest (RF); Support vector machine (SVM)
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	College of Engineering Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	06 Dec 2023 02:55
Last Modified:	06 Dec 2023 02:55
URI:	http://umpir.ump.edu.my/id/eprint/39523
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