Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction

By 2023, diabetes will have become a major public health problem in Pakistan, affecting an estimated 26.3% of the adult population. The rising death rate is attributable in part to diabetes-related complications, highlighting the critical need for cutting-edge, trustworthy diabetes prediction systems that enable early detection and treatment. Since machine learning (ML) models have the potential to improve the accuracy of diabetes forecasts, this study is focused on exploiting these methods of artificial intelligence (AI) for better predictions. As such, the importance of this research to the field of medical informatics cannot be overstated. A wide range of ML and deep learning models, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, Random Forests, Decision Trees, and a novel ensemble learning approach, were tested and compared. The models were evaluated on a conventional diabetes dataset. Our min-max normalization and other data pre-processing procedures greatly improved model accuracy. At first, the models were only about 70% accurate. However, after the pre-processing phase, we saw a dramatic improvement in performance. In particular, Random Forests reached 96.49 percent accuracy, while both Decision Trees and KNN models hit 96.33 percent. To improve upon the prediction accuracy of the separate models, we used a novel ensemble learning mechanism. This method demonstrates the efficacy of hybrid techniques in AI-driven healthcare solutions by utilizing the strengths of different ML models to create a single optimal model. Our research establishes a new paradigm for the field of illness prediction and has far-reaching consequences for diabetes care and early intervention techniques. We anticipate that our findings will encourage more investigation into the utility of cutting-edge AI techniques for improved disease prediction, ultimately leading to a more secure and manageable future.