Machine learning based predictive model of Type 2 diabetes complications using Malaysian National Diabetes Registry: A study protocol

Abstract

Background: The prevalence of diabetes in Malaysia is increasing, and identifying patients with higher risk of complications is crucial for effective management. The use of machine learning (ML) to develop prediction models has been shown to outperform non-ML models. This study aims to develop predictive models for Type 2 Diabetes (T2D) complications in Malaysia using ML techniques. Design and methods: -fold cross-validation technique. The best model for each algorithm is evaluated on a hold-out dataset using multiple metrics. Expected impact of the study on public health: The prediction model may be a valuable tool for diabetes management and secondary prevention by enabling earlier interventions and optimal resource allocation, leading to better health outcomes.

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