Optimizing Diabetes Control: The Role of Machine Learning in Disease Management

The article deals with four of the most widely used models, including Decision Tree, Random Forest, SVM, and Logistic Regression, regarding their applicability to diabetes prediction and management. This research assesses the predictive capabilities of such models in predicting diabetes based on several performance measures such as accuracy, precision, recall, F1-score, and AUC-ROC while checking the relative importance of some of the main features such as Age and BMI in the task of diabetes prediction. However, it is determined that Random Forest and SVM results have surpassed those of other models and Random Forest demonstrated the superior performance. Still, this article mentioned that such studies were related to real-time challenges of diabetic care due to improper data, difficulty in explanation, and social problems such as data privacy, and bias, in applying ML models. The findings indicate that machine learning can significantly improve diabetes management, but its integration into healthcare systems requires further research and development. Future work should focus on the incorporation of deep learning, personalized medicine, and wearable technologies to improve prediction accuracy and patient outcomes.