Decision Support Predictive Model for Prognosis of Diabetes Using PSO-Based Ensemble Learning

The decision support predictive model for diabetes diagnosis is a valuable tool that can help healthcare professionals accurately predict diabetes outcomes and deliver the finest possible treatment to their patients. The main research purpose is to detect and classify the diabetes image by utilizing an ensemble learning method. The system uses a combination of techniques for feature selection and classification to detect the presence of diabetes, and we used SMOTE analysis to balance data from imbalanced data. The feature selection technique is used to identify the relevant factors that are associated with diabetes with the help of PSO. The model's performance is assessed utilizing measures like accuracy, recall, and F1-score. Results demonstrate the feasibility of recommended system in predicting a diabetes presence. The suggested system can be used as an effective decision support tool for early diagnosis and treatment of diabetes.