Review Paper on Diabetes Prediction

Millions of people all over the world suffer from diabetes, a chronic disease that requires early checkups and efficient treatment. Clinical diagnosis techniques depend on clinical assessments and blood testing, which can be expensive and timeconsuming for everyone. Predictive models have become effective instruments for early detection of diabetes as a result of developments in artificial intelligence and machine learning. This study analyzes several machine learning methods for diabetes prediction, such as logistic regression, decision trees, support vector machines, and deep learning. It also examines datasets that are widely used, including real-time health monitoring systems and the Pima Indian Diabetes Dataset (originally from the National Institute of Diabetes and Digestive and Kidney Diseases). This review addresses various challenges such as data privacy, inaccurate models, and lack of result interpretability It also emphasizes the need for integrating these predefined predictive models into clinical approaches to enhance patient outcomes and make the process more efficient. Feature selection, data imbalance, and model clarity are also discussed while focusing on the models' advantages and disadvantages. Enhancing real-world applicability, incorporating data from wearable gadgets, and increasing model accuracy are suggested as future directions. This study aims to provide a clearer understanding of the current landscape and contribute to more efficient and accurate solutions in diabetes prediction