Interpretable Ensemble Model-Agonistc Approaches for Malaria Prediction

Abstract

Healthcare workers and patients alike face the problem of inadequate diagnosis, poor risk assessment, inadequate treatment, or toxic medication that puts patients at risk of higher death and morbidity in areas where malaria is endemic. For the past ten years, efforts to battle malaria have led to the creation of a variety of strategies, including studies on the disease that focus primarily on the automation of tools for diagnostic testing (RDT) and microscope imaging. In our work, we provide various ensemble learning techniques that provide explainability and lead to better malarial status prediction. The study focused on ensembling different individually trained models best on their accuracy performance. The results of disease prediction were made transparent and comprehensible by using explainable AI approaches like Explain Like I’m 5 (ELI5) and Local Interpretable Model-agnostic Explanation (LIME). Compared to other ensemble models, Bagging and Stacking ensemble models yielded a 99% performance. The application of these models could have a substantial influence on healthcare by decreasing the use of over-the-counter self-medication, improving patient outcomes by accurately classifying malaria cases, and increasing the effectiveness of early prediction accuracy. The explainability of the Ensemble models bolsters the relevance. Our research study enhances the field of interpretable machine learning models for medical contexts and makes a significant contribution to global public health initiatives by expanding our understanding of these models.