Performance Analysis and Accuracy of Machine Learning Algorithms for Heart Disease Prediction

Silpani Yuliasari; Alam Rahmatulloh

doi:10.31315/telematika.v22i3.14022

Authors

Silpani Yuliasari Universitas Siliwangi
Alam Rahmatulloh

DOI:

https://doi.org/10.31315/telematika.v22i3.14022

Keywords:

Heart Disease Prediction, K-Nearest Neighbors, Logistic Regression, Neural Network, Random Forest

Abstract

Purpose: This research aims to analyze the performance and accuracy of machine learning algorithms in predicting heart disease, which is a cause of death throughout the world.

Design/methodology/approach: The algorithms analyzed include Logistic Regression, Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, XGBoost, and Neural Network. A publicly available dataset containing patients' medical records was utilized, with the methodology encompassing data collection, Exploratory Data Analysis (EDA), model training, and performance evaluation.

Findings/result: The results indicate that the Random Forest algorithm achieved the highest accuracy with an accuracy of 90.16%, followed by Logistic Regression and Naive Bayes with accuracies of 85.25%. The K-Nearest Neighbors algorithm exhibits the lowest accuracy at 67.21%.

Originality/value/state of the art: This research highlights the advantages of certain machine learning algorithms in predicting heart disease and contributes knowledge to early detection technology in the health sector.

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