Optimizing Diabetes Prediction Using Hybrid Ensemble Learning Model

Diabetes is a time-transcending sickness that happens when your body can't make or use insulin well. This can harm many body parts like your heart, blood vessels, eyes, kidneys, and nerves. Finding diabetes early is crucial to treat it fast and stop it from getting worse. But regular computer programs don't do a great job at predicting what will happen with diabetes because they struggle with some data issues. In our research, we looked at a special kind of computer program called stacking-bagging, which combines different types of programs to make predictions. We used it along with Support Vector Machine (SVM) on a famous dataset called Pima Indian diabetes dataset. After training and testing our models a lot, we found out that our new method worked really well. It got 90.25% of predictions right and was very good at being precise and remembering things too. In fact, it did better than other fancy computer programs people use for this problem.

• diabetes prediction
• ensemble learning
• support vector machine
• Pima Indians diabetes dataset
• early detection of diabetes
• model accuracy

• 3 — Bonne santé et bien-être

• Artificial Intelligence in Healthcare
• Data Mining and Machine Learning Applications
• Imbalanced Data Classification Techniques
• diabetes prediction
• ensemble learning
• support vector machine
• Pima Indians diabetes dataset
• early detection of diabetes
• model accuracy
• Health Information Management
• Information Systems
• Artificial Intelligence
• Health Professions
• Computer Science