A Comparative Analysis of Machine Learning Models for Predicting Thyroid Disorders in Type 1 and Type 2 Diabetic Patients

Machine learning (ML) is increasingly indispensable in modern medicine, particularly for disease prediction and improving patient outcomes. This study applies ML techniques to predict thyroid disorders in diabetic patients, a critical task given the frequent co-occurrence and complex interplay between these conditions. six ML classifiers namely Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) were evaluated across three experiments on a local dataset: (1) a balanced dataset using Random Under-Sampling (RUS), (2) a subset of Type 2 diabetes (T2D) patients, and (3) a subset of Type 1 diabetes (T1D) patients. Random Forest classifier consistently outperformed other classifiers, achieving the highest accuracy (0.85) and F1-score (0.83) in the T2D-focused dataset and showing robust performance on the balanced dataset using RUS. These results highlight the suitability of Random Forest for deployment in clinical settings and underscore the importance of balancing techniques like RUS in improving predictive accuracy. However, challenges remain in predicting thyroid disorders among T1D patients due to the low prevalence of thyroid disorders in this group. The findings reinforce the potential of ML in advancing diagnostics and personalized care in diabetic populations.