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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika SemanTIK : Teknik Informasi IT JOURNAL RESEARCH AND DEVELOPMENT Techne : Jurnal Ilmiah Elektroteknika Jurnal Sistem Cerdas PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Applied Technology and Computing Science Journal Letters in Information Technology Education (LITE) International Journal of Informatics Engineering and Computing
Ordiyasa, I Wayan
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Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Comparative Analysis of Kidney Disease Detection Using Machine Learning DIQI, MOHAMMAD; ORDIYASA, I WAYAN; HISWATI, MARSELINA ENDAH
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 15, No 2 (2023): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v15i2.21468

Abstract

This research aimed to compare the performance of ten machine learning algorithms for detecting kidney disease, utilizing data from UCI Machine Learning Repository. The algorithms tested included K-Nearest Neighbour, RBF SVM, Linear SVM, Neural Net, Decision Tree, Naïve Bayes, AdaBoost, Random Forest, Gaussian Process, and QDA. The evaluation metrics used were accuracy, precision, recall, and F1-score. The findings revealed that AdaBoost was the most effective algorithm for all evaluation metrics, achieving an accuracy, precision, recall, and F1-score of 1.00. Random Forest and RBF followed closely, while Naïve Bayes and QDA had the lowest performance. These results suggest that machine learning algorithms, especially ensemble methods such as AdaBoost, can significantly improve the accuracy and efficiency of detecting kidney disease. This can lead to better patient outcomes and reduced healthcare costs.
Co-Authors Dian Rhesa Rahmayanti Elisabeth Deta Lustiyati Evrita Lusiana Utari, Evrita Lusiana Fathinah, Azzah HAFIZAH, IDA Hamzah Hamzah Hamzah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mohammad Diqi Noverianus, Noverianus Putra Wanda, Putra Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sriwidodo Sriwidodo, Sriwidodo Sugeng Winardi Sugiarto, Raden Bagus Nurhadi Wijaya Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Wijaya, Nurhadi Wiratma, Harits Dwi