Pattun - Department of Computer Science and Information Technology, School of Technology, Maulana Azad National Urdu University, Hyderabad, Telangana, India-۵۰۰۰۳۲.
Afroaz - Department of Computer Science and Information Technology, School of Technology, Maulana Azad National Urdu University, Hyderabad, Telangana, India-۵۰۰۰۳۲.
Siddiqui - Department of Computer Science and Information Technology, School of Technology, Maulana Azad National Urdu University, Hyderabad, Telangana, India-۵۰۰۰۳۲.
Ghazala - Department of Computer Science and Information Technology, School of Technology, Maulana Azad National Urdu University, Hyderabad, Telangana, India-۵۰۰۰۳۲.

Diseases like diabetes are chronic and require long-term management. Inadequate production of insulin results in high blood sugar levels. Such diseases lead to serious health issues such as heart ailments, blood vessel complaints, eye ailments, kidney function disorders, and nerve ailments. Hence, accurate assessment and management of risk factors are crucial for the onset of diabetes. Our proposed approach combines fuzzy logic & machine learning algorithms for diabetes risk prediction. Three machine learning models were trained to classify patients into two categories of diabetes (Type-I and Type-II) based on their clinical dataset collected from Katihar Medical College & Hospital and Suvadhan Lab. The polynomial regression algorithm achieved a score of ۰.۹۴۷, while the support vector regression algorithm with the rbf kernel achieved a score of ۰.۹۵۴, with a linear kernel achieved a score of ۰.۷۳. Our proposed approach performed well with respect to the conventional approaches with improved accuracy by identifying the patients at diabetes risk. In future work, we further analyze the relationship between other ignored factors which contribute to diabetes risk.

diabetes, Blood sugar, Machine Learning Algorithm, Fuzzy logic, Disease Management, risk factors, insulin resistance, polynomial regression, Support Vector Regression