Role of Explainable Artificial Intelligence (XAI) in Prediction of Non-Communicable Diseases (NCDs)

Abstract

Explainable artificial intelligence (XAI) concentrated on methods and models that simplify the comprehending and analysis of the ML models operation. Using XAI, systems deliver the essential facts to defend outcomes, mostly when unpredicted conclusions are made. It also certifies that there is an auditable and demonstrable way to guard algorithmic judgments including the factors of unbiased and being principled, which lead to building trust. Swift upsurge of non-communicable diseases (NCDs) turns out to be one of the severe health matters and one of the leading origins of death globally. In this chapter, the authors discussed XAI in healthcare, its benefits, and the deep Shapley additive explanations (DeepSHAP)-based deep neural network (DeepNN) framework provided with a feature selection method for prediction and explanation of non-communicable diseases followed by case study discussion about detection and progression of Alzheimer's disease (AD) with the help of XAI-based predictive models.