IMPROVING HEART DISEASE PREDICTION ACCURACY USING PRINCIPAL COMPONENT ANALYSIS (PCA) IN MACHINE LEARNING ALGORITHMS

Zirji Jayidan; Amril Mutoi Siregar; Sutan  Faisal; Hanny Hikmayanti

doi:10.52436/1.jutif.2024.5.3.2047

Zirji Jayidan Informatics Departement, Faculty of Computer Sciences, Universitas Buana Perjuangan Karawang, Indonesia
Amril Mutoi Siregar Informatics Departement, Faculty of Computer Sciences, Universitas Buana Perjuangan Karawang, Indonesia
Sutan Faisal Informatics Departement, Faculty of Computer Sciences, Universitas Buana Perjuangan Karawang, Indonesia
Hanny Hikmayanti Informatics Departement, Faculty of Computer Sciences, Universitas Buana Perjuangan Karawang, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.2047

Keywords: Diagnostic Accuracy, Feature Extraction, Heart Disease Prediction, Machine Learning Algorithm, Principal Component Analysis (PCA)

Abstract

This study aims to improve the accuracy of heart disease prediction using Principal Component Analysis (PCA) for feature extraction and various machine learning algorithms. The dataset consists of 334 rows with 49 attributes, 5 classes and 31 target diagnoses. The five algorithms used were K-nearest neighbors (KNN), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Results show that algorithms using PCA achieve high accuracy, especially RF, LR, and DT with accuracy up to 1.00. This research highlights the potential of PCA-based machine learning models in early diagnosis of heart disease.

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