IMPROVING HEART DISEASE PREDICTION ACCURACY USING PRINCIPAL COMPONENT ANALYSIS (PCA) IN MACHINE LEARNING ALGORITHMS - Jayidan, Zirji; Siregar, Amril Mutoi; Faisal, Sutan; Hikmayanti, Hanny (Jurnal Teknik Informatika (Jutif), 2024)
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.