Explainable Artificial Intelligence (EAI) Based Disease Prediction Model

Early disease prediction is a critical area of focus in healthcare. Identifying diseases at an early stage can increase the chances of successful treatment and reduce healthcare costs. Artificial Intelligence (AI) techniques like NLP, speech recognition, and machine vision can be used to predict and diagnose diseases. However, traditional AI methods can be error-prone. Explainable AI (EAI) techniques can reduce detection errors and improve prediction accuracy. This study proposes an EAI model for disease prediction using eSHAP. ESHAP can explain how a model arrives at a prediction, making it easier to understand and validate. The proposed model may provide better performance in accurate disease prediction. AI and EAI techniques have significant potential to revolutionize disease prediction, early detection, and treatment, ultimately leading to improved health outcomes for patients.