Models of Machine Learning to Diagnose Chronic Kidney disease using a  WEKA-based Classifier

Authors

• Shaymaa Adnan Abdulrahman, Business Information College, University of Information Technology and Communication, Baghdad, Iraq.Follow
• Sameerah Faris Khlebis, Business Information College, University of Information Technology and Communication, Baghdad, Iraq.

Abstract

In the present day, humans are confronted with a variety of diseases as a result of their lifestyle and the current environmental conditions. Therefore, it is crucial to identify and predict these diseases in their early phases in order to prevent their severe manifestations. Manually identifying maladies is a challenging task for physicians on a regular basis. Predicting chronic illnesses is the aim of this article. This goal is applicable through a state-of-the-art approach to classification correctly identifies people with chronic illnesses. Predicting maladies is also a difficult endeavor. Therefore, disease prediction is significantly influenced by data mining. To get data, a collection of disease symptoms, the individual's lifestyle, and information regarding medical consultations are considered in this general disease prediction. In conclusion, this paper analyzes that model with a variety of algorithms, including (Naiva Bayes) and (RF-Random Forest).

Recommended Citation

Abdulrahman, Shaymaa Adnan and Khlebis, Sameerah Faris (2025) "Models of Machine Learning to Diagnose Chronic Kidney disease using a  WEKA-based Classifier," Mesopotamian Journal of Artificial Intelligence in Healthcare: Vol. 3: Iss. 1, Article 4.
DOI: https://doi.org/10.58496/MJAIH/2025/005

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.