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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Didik Setiyadi
STMIK Sinar Nusantara
Computer Science & IT

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Prediction of heart disease using random forest algorithm, support vector machine, and neural network Didik Setiyadi; Henderi Henderi; Anrie Suryaningrat; Rulin Swastika; Saludin Saludin; Muhamad Malik Mutoffar; Imam Yunianto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 1: February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i1.25341

Abstract

The heart is a vital organ responsible for pumping blood throughout the human body. Machine learning has become an increasingly important tool in medical forecasting, improving diagnostic accuracy and reducing human errors. This study focuses on detecting heart disease using machine learning algorithms. It aims to compare the performance of three key algorithms random forest (RF), support vector machine (SVM), and neural networks (NN), in predicting heart disease. Using a patient dataset with both nominal and numeric attributes, record mining techniques were applied through Orange software. The target classes indicated the absence (0) or presence (1) of heart disorders. The evaluation was based on the prediction accuracy of each algorithm. Results show that SVM achieved the highest accuracy, with a rate of 85%, outperforming RF and NN. The findings suggest that the SVM algorithm is a reliable tool for heart disease prediction, helping reduce diagnostic errors and improve medical decision-making.
Co-Authors Anrie Suryaningrat Henderi . Imam Yunianto Muhamad Malik Mutoffar Rulin Swastika Saludin Saludin