Multi-Type Diabetes using Machine Learning – A New Method of Prediction and Determination

Efficient techniques for classifying diabetes types are essential for effective treatment of this chronic condition. Artificial intelligence (AI) algorithms, particularly Support Vector Machine (SVM) algorithms, have shown great promise in this area. A recent study utilized SVMs with different functions, including linear, polynomial, and sigmoid, to categorize diabetes into distinct types. The dataset was divided into training and test sets to perform prediction analysis, with the SVM classifier applied to both sets. The proposed system excels in segregating data for processing, training the model to accurately differentiate between Type 1 and Type 2 diabetes. By leveraging AI-based algorithms, the system enhances the precision of diabetes classification, thereby facilitating more tailored and effective treatment plans for patients. The implementation of SVM algorithms in diabetes classification not only demonstrates the capabilities of AI in medical diagnostics but also underscores the potential for integrating advanced computational techniques in healthcare. The study's approach ensures that the classifier can handle diverse patient data, improving its generalizability and robustness. As diabetes cases continue to rise globally, such AI-driven solutions could play a pivotal role in early diagnosis, personalized treatment, and ongoing management, ultimately contributing to better health outcomes and reduced healthcare costs.