Chronic Kidney Disease Prediction Model Using Naïve Bayes (Case Study: Jayapura City)

Authors

  • Grace Adelin Rumbairusy Rumbairusy Universitas Sepuluh Nopember Papua, Indonesia
  • Manda Manda Universitas Flores, Nusa Tenggara Timur, Indonesia
  • Yulan Nanda Sandira Payungallo Universitas Flores, Nusa Tenggara Timur, Indonesia
  • Heru Sutejo Universitas Flores, Nusa Tenggara Timur, Indonesia https://orcid.org/0009-0009-6836-5990

DOI:

https://doi.org/10.51903/k47t6677

Keywords:

Chronic Kidney Disease, Naïve Bayes, RapidMiner, Medical Prediction, Data Mining

Abstract

Chronic Kidney Disease (CKD) remains one of the most critical global health issues due to its progressive nature and the absence of early symptoms that often lead to late diagnosis. In Jayapura City, the number of CKD cases continues to increase, driven by factors such as hypertension, diabetes, and limited access to early diagnostic services. This study aims to develop a predictive model for CKD using the Naïve Bayes algorithm and to analyze the relationship between clinical variables that contribute to CKD among patients in Jayapura. The research adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM), which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. A clinical dataset consisting of 500 patient records and 13 medical attributes—such as blood pressure, glucose level, serum creatinine, hemoglobin, albumin, and urine characteristics—was used. The dataset had been pre-cleaned before processing, ensuring it was ready for direct modeling in RapidMiner. The Naïve Bayes model achieved an accuracy of 94.40%, with substantial precision and recall values across both CKD and non-CKD classes. These findings indicate that Naïve Bayes is highly effective in identifying CKD patterns within local patient data. The main contribution of this study lies in the use of real clinical data specific to Jayapura, which provides insights into regional CKD characteristics and offers a reliable predictive tool to support early screening. The implications of this research highlight the potential integration of the model into clinical decision-support systems and community health monitoring applications, enabling earlier interventions and improved patient outcomes.

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Published

2025-12-30

Issue

Vol. 4 No. 1 (2025): Januari : Jurnal Ilmiah Sistem Informasi

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Copyright (c) 2025 Grace Adelin Rumbairusy Rumbairusy, Manda Manda, Yulan Nanda Sandira Payungallo, Heru Sutejo

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