Explainable Machine Learning Model for Chronic Kidney Disease Prediction

Abstract

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model’s transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model’s decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model’s limitations, and ascertain its reliability. By improving the model’s interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes.

Keywords

• explainable machine learning
• multi-layer perceptron
• chronic kidney disease
• healthcare predictive modeling