Improved Diabetes Prediction Through Multimodeling: Merging Diverse Machine Learning Models for Superior Performance

Diabetes mellitus is still a significant global health issue, and early diagnosis is important for better patient outcomes. Conventional machine learning models, though, tend to fail in dealing with the variability and complexity of medical data. In this research, we introduce a multimodeling approach that combines several machine learning models, such as Multilayer Perceptron (MLP), Decision Trees, Support Vector Machines (SVM), Logistic Regression, and Gradient Boosting, to improve the accuracy and reliability of diabetes prediction. Our method leverages state-of-the-art feature selection mechanisms to determine the most informative predictors, capturing both linear and non-linear trends in the data. By using an ensemble methodology, we leverage the strengths of deep models and conventional models while promoting generalization and enhancing overall performance. Our experimental outcomes on a widely available diabetes dataset confirm that our system performs better than single-model based methods in primary performance indicators, such as accuracy, precision, recall, and F1-score. The findings show that multimodeling offers a resilient and flexible strategy for diabetes prediction, utilizing shallow and deep learning methods. This research points out the potential of multimodeling to improve healthcare applications, facilitating early detection of diabetes and personalized treatment for patients.