Integrating Machine Learning Models for Accurate Prediction of Diabetes

The incidence of diabetes has been increasingly high, and this calls for a foresightful prediction that needs early intervention. The work done during the conducting of the research focuses on the integration of various machine learning models to predict the onset of diabetes with great accuracy. We have used different algorithms such as decision trees, random forests, support vector machines, and deep learning techniques in order to identify the best approach for the prediction of diabetes in this study. By using clinical datasets, we preprocessed and engineered the features of age, blood sugar level, BMI, and family history to be fed into the model in hopes of improving predictive performance. The experimental results show that models combining random forests with deep learning lead to better prediction accuracy for diabetes risk compared to single models. This paper contributes towards the development of the diabetes prediction model because it ensures that integration is of paramount importance for better diagnosis in clinical cases.