부산대학교 도서관

Chronic Kidney Disease (CKD) is an irreversible disease affecting millons of people all around the world. To this date, no cure has been produced for CKD and it is financially very challenging to treat this disease. The irreversible nature of the disease makes it critical to be analyzed by Machine Learning models. Since it is a significant healthcare research domain, the decision results (of patients having or not having the disease) by the Machine Learning models should be explained to the patients and the clinical practitioners. The scope of this research is to apply ensemble Machine Learning models on prediction of CKD from a dataset of 400 subjects. In the prediction analysis part, this research focuses on addressing key features leading to the prediction. The Explainable AI (XAI) techniques are implemented to explain the clinical practitioners about necessary changes required in the features for contrasting class prediction.