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N. Sravani is an Assistant Professor in the Department of Artificial Intelligence and Data Science at Sree Rama Engineering College, Tirupati, Andhra Pradesh, India. Her academic responsibilities include teaching and research in emerging computing technologies. Her departmental specialization encompasses artificial intelligence and data science. Her contribution focuses on computational approaches to diabetes prediction and risk assessment.
Diabetes mellitus is a persistent metabolic condition marked by increased blood sugar levels and linked to severe complications such as cardiovascular ailments, renal impairment, neuropathy, and retinopathy. Prompt detection of persons predisposed to diabetes is crucial for prompt intervention, efficient disease management, and the avoidance of chronic consequences. Artificial intelligence (AI), especially through machine learning and deep learning, offers avenues to scrutinise diverse healthcare data and discern patterns linked to diabetes risk. This chapter explores AI-driven methodologies for forecasting diabetes and tailoring risk evaluations, encompassing data preprocessing, feature selection, predictive modelling, risk stratification, explainable AI, and clinical decision assistance. A comprehensive AI-driven system is suggested to evaluate diabetes risk by utilising personal health data and producing clear, individualised risk evaluations.Nonetheless, AI-generated forecasts ought to augment, rather than supplant, conventional diagnostic methods and expert medical discernment.