Authors - Karuppasamy M, Jansi Rani M, Poorani K Abstract - Diabetes is the leading cause of mortality since its prevalence is higher globally. Since it contributes to various kinds of complications it leads to a high mortality rate. Early diagnosis and prediction of contributing features are found with the assistance of machine learning models. These models are instrumental in assisting healthcare sectors in prediction, diagnosis, prognosis, and disease prevention. If diseases are found at earlier stages, it would save many people’s lives. In that aspect, machine learning models are developed to find diseases at earlier stages. However, accuracy of the predictions at not much satisfied. This proposed work explores the techniques to predict diabetes at earlier stages. Several data mining approaches to XAI are discussed. The major features contributing to diabetes are also identified with the feature importance technique. This results in a greater way of understanding which feature contributes more to diabetic progression. The proposed model resulted in 94% accuracy with random forest which is also elaborated with Explainable AI (XAI).
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