Heart Disease Prediction Classification using Machine Learning
Shatendra Kumar Dubey1, Sitesh Sinha2, Anurag Jain3

1Shatendra Kumar Dubey, Ph.D., Research Scholar, Department of Computer Science Engineering, RNTU, Bhopal (M.P), India.

2Dr. Sitesh Sinha, Department of Computer Science Engineering, AISECT University, Bhopal (M.P), India.

3Dr. Anurag Jain, Department of Computer Science Engineering, REC, Bhopal (M.P), India.

Manuscript received on 12 October 2023 | Revised Manuscript received on 07 November 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023 | PP: 1-6 | Volume-10 Issue-11, November 2023 | Retrieval Number: 100.1/ijies.B43211213223 | DOI: 10.35940/ijies.B4321.11101123

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Abstract: Heart disease is a leading cause of mortality worldwide, and early detection and accurate prediction of heart disease can significantly improve patient outcomes. Machine learning techniques have shown great promise in assisting healthcare professionals in diagnosing and predicting heart disease. The diagnosis and prognosis of heart disease must be improved, refined, and accurate, because a small mistake can cause weakness or death. According to a recent World Health Organization study, 17.5 million people die each year. By 2030, this number will increase to 75 million.[2] This document explains how to enable online KSRM capabilities. The KSRM smart system allows users to report heart-related problems. This research paper aims to explore the use of machine learning algorithms for effective heart disease prediction classification with Adaboost for improve the accuracy of algorithm.

Keywords: K-NN, SVM, RF, LR, MLP, DT, NB, Adaboost.
Scope of the Article: Machine Learning