Rule Based Algorithms: A Comprehensive Review of the Modlem Method in the Prediction of Type-2 Diabetes

Type 2 - Diabetes Mellitus (T2-DM) is a prevalent and chronic metabolic syndrome with a growing global incidence. Early prediction of individuals at risk for developing T2-DM is essential for instigating precautionary measures and improving overall health outcomes. This study discovers the application of rule-based algorithms for predicting the onset of T2-DM based on various risk factors. A comprehensive dataset comprising demographic information, lifestyle factors, and clinical markers was utilized for model development. The rule-based algorithms, including decision trees, expert systems, and fuzzy logic, were employed to extract patterns and relationships within the data. The rules generated by these algorithms aimed to capture the complex interplay of factors contributing to T2-DM risk. The study involved the evaluation and comparison of different models in terms of accuracy, sensitivity, specificity, and interpretability. The dataset was divided into testing and training sets to assess the generalizability of the developed models. The attained results demonstrated promising predictive performance, with the rule-based models exhibiting competitive accuracy compared to traditional machine learning approaches. Moreover, the transparent and interpretable nature of rule-based algorithms allowed for a clear understanding of the decision-making process, facilitating the identification of actionable insights for healthcare practitioners.