Predicting Chronic Kidney Disease using ML algorithms and XAI

Sudip Raj Khadka¹, Saphal Subedi¹, Bikash Kumar Aidy¹, Lok Nath Regmi², Ashish Parajuli¹ and Mohan Bhandari¹*
¹Dept. of CSIT Samriddhi College Lokanthali, Bhaktapur, Nepal
²Dept. of Elx. and Comp. Engineering IOE-Pulchowk Campus Pulchowk, Lalitpur, Nepal
*Corresponding Author: Mohan Bhandari, Dept. of CSIT Samriddhi College Lokanthali, Bhaktapur, Nepal.

Published: May 30, 2023

DOI: 10.55162/MCET.04.142

View Pdf

Abstract  

Kidney disease is a significant health concern that is currently affecting individuals of all age groups. To predict the occurrence of chronic renal disease, a large number of scholars have employed machine learning and deep learning techniques. However, the efficacy of these methods is often hampered by a lack of transparency, which is a major issue in the application of artificial intelligence in healthcare and medical analysis. As such, the lack of clarity has prompted concern. To interpret the results of predictive models, the present study proposes the deployment of four machine learning algorithms, including Decision Tree, Logistic Regression, Multi-layer Perceptron Classifier, and Support Vector Machine, in combination with explainable AI (XAI) interface, leveraging the local interpretable model-agnostic explanation (LIME) and shapely additive explanation shapely additive values (SHAP). The proposed models are intended to facilitate effective decision-making in clinical research and therapeutic practices.

Keywords: Machine Learning; Decision Tree Classifier; Logistic Regression; Support Vector Machine; Explainable AI; SHAP; LIME; Chronic Kidney Disease