A Comprehensive Comparative Study of Machine Learning Methods for Chronic Kidney Disease Classification: Decision Tree, Support Vector Machine, and Naive Bayes

Based on the findings of the 2010 Global Burden of Disease analysis, there was an increase in the global ranking of Chronic Kidney Disease (CKD) as a major contributor to mortality, moving from 27th place in 1990 to 18th position. Approximately 10 percent of the global population experiences CKD, and every year millions of lives are lost due to limited access to adequate treatment. CKD poses a substantial global health concern, greatly affecting both the well-being and life span of individuals afflicted by the condition. This study aims to evaluate the performance of three major classification algorithms in CKD diagnosis: Decision Tree, Support Vector Machine (SVM), and Naïve Bayes. This research distinguishes it from previous studies through an innovative data processing approach. Data preprocessing involved transforming categorical values into numerical form using label encoding, as well as applying Exploratory Data Analysis (EDA) to identify outliers and test data assumptions. In addition, the handling of missing values was done with appropriate strategies to maintain the integrity of the dataset. The classification method was evaluated using a dataset of 400 samples from Kaggle with 24 attributes. Through careful experimentation, the accuracy results of each algorithm are presented and compared. The results of this study can help in the development of a more efficient and accurate decision support system for the early diagnosis of CKD.

@article{Syarif2023,
  title     = {A Comprehensive Comparative Study of Machine Learning Methods for Chronic Kidney Disease Classification: Decision Tree, Support Vector Machine, and Naive Bayes},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {10},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Admi Syarif and Olivia Desti Riana and Dewi Asiah Shofiana and Akmal Junaidi},
  doi       = {10.14569/IJACSA.2023.0141063},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141063}
}