Predicting Chronic Kidney Disease using ML algorithms and LIME

Abstract

Numerous researchers have implemented machine learning (ML), and deep learning (DL) to predict chronic kidney diseases (CKD). But these studies have succeeded in early diagnosis, they lack transparency. Such ambiguity has raised red flags in adopting AI in the critical domain of healthcare and medical analyses. This paper aims at interpreting the outcome of predictive models by proposing an explainable AI (XAI) interface using local interpretable model-agnostic explanation (LIME). The intended model aims to hold the system accountable for its projections, which will assist efficient decision-making in the field of clinical research and therapeutic practice.