Risk Prediction of Diabetic Disease Using Machine Learning Techniques

The type of nutrition we are receiving today, collectively with our inconsistent dietary habits and schedules, are important contributors to the rising prevalence of diabetes. The main causes of diabetes are obesity, high blood sugar, and other parameters. With a focus on the Pima Indian Diabetes dataset, this research study gives a thorough investigation of predictive modeling for diabetes using machine learning approaches. We thoroughly assess different classification algorithms, such as logistic regression, K-nearest neighbors (KNN), random forest, decision trees, Naive Bayes, and support vector machine (SVM), for effectiveness in early diabetes detection with data. Best practices for feature selection, data preprocessing, and model evaluation guide our methodical approach. Results show that machine learning has the potential to improve healthcare decision-making by giving physicians trustworthy tools for identifying people at risk for diabetes. This research advances the utilization of machine learning in healthcare.