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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi JURNAL SIMTIKA (Sistem Informasi dan Informatika) Journal of Tourism Sciences, Technology and Industry
Amuharnis Amuharnis
Universitas Metamedia
Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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Journal : Journal of Tourism Sciences, Technology and Industry

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Classification of Chronic Kidney Disease based on health care records using machine learning with Support Vector Machine Abdurrahman Niarman; iswandi iswandi; Amuharnis Amuharnis
Journal of Tourism Sciences, Technology and Industry Vol 2, No 2 (2023): JTSTI-Journal Of Tourism Science, Technology and Industry
Publisher : Institut Seni Indonesia Padangpanjang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26887/jtsti.v2i2.4080

Abstract

Chronic Kidney Disease (CKD) is a global health concern with a rising prevalence that necessitates early and accurate diagnosis for effective management. This study proposes the application of Machine Learning (ML), specifically Support Vector Machine (SVM), to classify CKD based on health care records. Leveraging a comprehensive dataset of patient health records, including clinical and demographic information, the research aims to develop a predictive model that can assist in the timely identification of individuals at risk of CKD. The methodology involves preprocessing the health care records, extracting relevant features, and implementing the SVM algorithm for classification. The dataset is divided into training and testing sets to evaluate the model's performance.  The SVM classification model that was developed after going through the data preprocessing process produced results that were good enough to be able to classify whether a patient was diagnosed with CKD or not with an accuracy level of 98% and a total of 400 lines of data and 25 features.
Co-Authors Abdurrahman Niarman Dilson Dilson Indra Ramadani iswandi iswandi Lakry Maltaf Putra Rahmadini Darwas Ratih Purwasih S Suardinata