Predictive Modeling for Type-3 Diabetes: A Machine Learning Approach for Healthcare Management

Diabetes Mellitus (DM) is a prevalent metabolic illness that needs to be accurately diagnosed and predicted in order to be effectively managed. Using datasets including the NC State University Diabetes Dataset and the Ohio Longitudinal 2023 Dataset, this work examines predictive modeling techniques for diabetes classification and Type-3 Diabetes (T3DM) forecasting. We use an assortment of machine learning methods, preprocess data, and perform exploratory data analysis. The hybrid technique achieves the most accuracy of 81.1% for diabetes classification. It does this by combining the predictions of gradient boosting and logistic regression classifiers. Furthermore, we create a T3DM predictive model that has outstanding predictive ability to identify people who are at risk. The hybrid algorithm's application demonstrates how well it works to increase prediction accuracy over the use of separate models. Our findings highlight the value of predictive modeling in managing diabetes, assisting in the making of healthcare decisions, and enhancing patient outcomes. Subsequent investigations may concentrate on incorporating supplementary data sources and utilizing sophisticated techniques to improve clinical relevance and predictive precision in the treatment of diabetes.