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Erschienen in:
2024 | OriginalPaper | Buchkapitel
An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting
verfasst von : Gifty Roy, Reshma Rachel Cherish, Boppuru Rudra Prathap
Erschienen in: Computational Sciences and Sustainable Technologies
Verlag: 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.