AI-Driven Early Diabetes Prediction

Artificial Intelligence (AI) and machine learning (ML) offer a significant advantage in early diabetes prediction, enabling proactive healthcare interventions that can mitigate the risk of complications. In this review, we explore various AI-driven approaches to predict diabetes, focusing on a project that implemented multiple ML models, including Support Vector Machines (SVM), Gradient Boosting, K-Nearest Neighbors (KNN), Naive Bayes and Logistic Regression. The KNN model demonstrated the best overall performance, achieving an accuracy score of 75%, as evidenced by other metrics such as precision, recall, F1-score, and AUC-ROC, leading to its deployment in a real-world application using Flask, a lightweight web framework. This deployment allows for real-time diabetes risk assessment, offering healthcare providers and patients an accessible tool for early diabetes prediction. However, the project also identified challenges related to data quality and model bias, underscoring the importance of addressing these limitations for broader adoption of AI-driven healthcare tools. This review concludes by discussing future directions for AI in healthcare, including potential improvements to diabetes prediction models and ethical considerations. The outcomes suggest that AI can play a crucial role in managing chronic diseases like diabetes, offering a pathway toward improved patient outcomes through early detection and personalized treatment.