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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) LINK Jurnal Transformatika SemanTIK : Teknik Informasi SISFOTENIKA Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Jurnal Pengabdian Dharma Bakti Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Berita Kedokteran Masyarakat Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
Marselina Endah Hiswati
Universitas Respati Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Other

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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 ADNAN ADNAN ABIDIN Ahmad Wakhid Ariani, Rahayu Cahya Arum Kurnia Sulistyawati Azijah, Izattul Basuki, Umar Dian Rhesa Rahmayanti Eka Damayanti Elisabeth Deta Lustiyati Erizal Erizal Fajar Y Zebua Fathinah, Azzah FAUZIYYAH, ANNUR HAFIZAH, IDA Hamzah Hamzah Hamzah Hamzah Huang J. Jie I Wayan Ordiyasa I Wayan Ordiyasa Kristian, Tadem Vergi Lestari, Nia Rizqi Lestariningsih Lestariningsih Lila Retnani Utami Lutfi Nur Wicaksono Meliala, Dyan Avando Mohammad Diqi Muflih Muflih Muflih, Muflih Ngaisyah, Rr Dewi Noverianus, Noverianus Ordiyasa, I Wayan Putra Wanda Putra Wanda Putra Wanda Putra Wanda, Putra Rahayu Widaryanti Rainbow Tambunan Romana Herlinda Salsabela, Marcella Sapariyah, Sapariyah Sri Hasta Mulyani Sriwidodo Sriwidodo, Sriwidodo Subandi, Yeyen Sudiyatmi, Sudiyatmi Supardi RS Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Wijaya, Nurhadi Wiratma, Harits Dwi