Predictive Modelling And Artificial Intelligence Strategies For Early Identification Of Type 2 Diabetes Risk: A Review
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Abstract
Type 2 Diabetes Mellitus (T2DM) is an alarmingly emerging issue in the health care of the world and needs effective measures to be taken to detect and prevent this deadly illness at an early stage. Recent progress in artificial intelligence (AI) and predictive modelling has led to the creation of intelligent systems that can be used to identify persons at risk before the clinical manifestations of the condition develop. In this review paper, it is observed that machine learning and deep learning techniques play a vital role in the early prediction of Type 2 Diabetes through clinical, lifestyle, and behavioural data. The research also emphasizes frequently employed predictive algorithms, the most important evaluation metrics, and the opportunities of AI-powered systems to facilitate an early risk evaluation and preventive health. Also, the paper addresses significant issues in AI-based diabetes prediction such as imbalanced data, insufficient feature integration, inability of models to provide interpretability, and the insufficient use of them in practice. The review highlights the importance of creating interpretable, scalable, and generalizable AI-based predictive models to improve screening in its initial phases and decrease the burden of Type 2 Diabetes in the world.
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