AI-Based Predictive Model for Accurate Diabetes Detection

Abstract

Diabetes mellitus is a chronic metabolic disorder that has millions of people in the entire world being admitted in hospitals across the globe due to insulin deficiency or resistance. Due to that, it is among the major causes of severe complications, such as cardiovascular disease, kidney failure, neuropathy and vision loss, including Diabetic Retinopathy. The most important step is early and correct diagnosis so as to stop the spread of the disease and avoid these debilitating consequences of the disease. Conventional care procedures can be invasive, inconvenient, time-consuming and imaginative besides being confined depending on the region of concern, especially the underserved which implies that having scalable thoughtful solutions that automatically initiate health care interventions is vital. Artificial Intelligence (AI) and Machine Learning (ML) have promised to transform screening processes in a way that will lead to enhanced patient outcomes by enabling examination of large, complicated datasets to validate the presence of subtle patterns, and make appropriate predictions.1 The following paper will synthesize studies into several types of AI-driven attempts, including those models based on organized clinical data, imaging-based diagnostics (retinal and tongue images), and real-time monitoring (wearable technology). The discussion will entail the discussion of the performance of various ML and Deep Learning algorithms, what makes the process of sound data preprocessing another critical factor, and why Explainable AI (XAI) is necessary to build trust and clinical reception. The results show that there are meaningful accuracy gains and powerful features over diabetes prediction and show that AI can play an increasingly destructive role in diabetes management.