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All Journal Indonesian Journal of Data and Science
Humam Maulana Tsubasanofa Ramadhan
Universitas Pembangunan Nasional
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Comparison of K-Nearest Neighbor and Decision Tree Methods using Principal Component Analysis Technique in Heart Disease Classification Al Danny Rian Wibisono; Syahrul Hidayat; Humam Maulana Tsubasanofa Ramadhan; Eva Yulia Puspaningrum
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.70

Abstract

Heart disease has become a global health issue that can threaten anyone, regardless of age. Numerous research efforts have been made to develop classification methods that can aid in diagnosing heart disease. In this study, we compared two classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree, by applying Principal Component Analysis (PCA) technique to the heart disease classification. The dataset used contains relevant clinical attributes. After analyzing the dataset and performing data preprocessing, we applied PCA to reduce the dataset's dimensions. PCA models with KNN and Decision Tree were implemented and evaluated using performance metrics such as Confusion Matrix, F1 Score, and Accuracy. The analysis results showed that the PCA model with Decision Tree outperformed the PCA model with KNN in terms of accuracy. The Decision Tree model successfully classified all data correctly, while KNN had some misclassifications. This research recommends using the PCA model with Decision Tree for heart disease classification with the best performance. However, further research with larger datasets is needed for a deeper understanding
Co-Authors Al Danny Rian Wibisono Eva Yulia Puspaningrum Syahrul Hidayat