Predicting Diabetes Risk Using Machine Learning Algorithms

This project employs machine learning techniques to predict diabetes. Decision trees, Support Vector Machines (SVM), and logistic regression are used to examine patient data, including age, family history, BMI, and glucose level. Through predictive analysis, the program determines high-risk profiles and forecasts the likelihood of diabetes. High accuracy in early diabetes identification is achieved by this model, which was trained using a medical dataset that is openly accessible. A useful resource for preventative care is made available by incorporating this predictive tool into healthcare apps. Additionally, the model can be utilized as a decision support tool to help clinicians with early intervention and therapy.