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2024 | OriginalPaper | Chapter

An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting

Authors : Gifty Roy, Reshma Rachel Cherish, Boppuru Rudra Prathap

Published in: Computational Sciences and Sustainable Technologies

Publisher: Springer Nature Switzerland

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Abstract

The risk of dying from a heart attack is high everywhere in the world. This is based on the fact that every forty seconds, someone dies from a myocardial infarction. In this paper, heart attack is predicted with the help of dataset sourced from UCI Machine Learning Repository. The dataset analyses 13 attributes of 303 patients. The categorization method of Data Mining helps predict if a person will have a heart attack based on how they live their lives. An empirical and statistical analysis of different classification methods like the Support Vector Machine (SVM) Algorithm, Random Forest (RF) Algorithm, K-Nearest Neighbour (KNN) Algorithm, Logistic Regression (LR) Algorithm, and Decision Tree (DT) Algorithm is used as classifiers for effective prediction of the disease. The research study showed classification accuracy of 90% using KNN Algorithm.

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Dataset considered is from UCI Machine Learning. https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Title: An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting
Authors: Gifty Roy
Reshma Rachel Cherish
Boppuru Rudra Prathap
Publisher: Springer Nature Switzerland
Book: Computational Sciences and Sustainable Technologies
Print ISBN: 978-3-031-50992-6

Electronic ISBN: 978-3-031-50993-3

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-3-031-50993-3_28

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