Reviewing Predictive Modeling for Type-3 Diabetes: Challenges and the Path Forward

The growing number of people affected by diabetes and its complications calls for new ways to detect, manage, and treat the condition early. This paper presents a machine learning-based framework designed to predict and classify Type 1, Type 2, and Type 3 diabetes with improved accuracy while addressing key gaps in current research. The framework combines data types, such as clinical records, demographic details, and biosignal data from wearable devices, to ensure a comprehensive and reliable approach. It also uses advanced data processing methods like feature selection, dimensionality reduction, and handling imbalanced datasets to improve model performance. The paper explores machine learning to highlight the importance of real-time monitoring, tracking data over time, and creating personalized risk assessments, especially for pregnant women. Additionally, it addresses practical challenges like making the models scalable, considering ethical issues, and integrating socioeconomic and biosignal data. Evaluation metrics like accuracy, precision, recall, and F1-score may ensure the model’s reliability and effectiveness. This paper shows how machine learning can revolutionize diabetes care by providing early warnings, reducing complications, and offering actionable insights for better management. By combining advanced algorithms with real-world usability, the proposed framework bridges the gap between research and practical healthcare, offering a scalable solution for improved diabetes management.