/Improving-Machine-Learning-Techniques-for-Diabetic-Prediction

This study aims to predict Type-2 Diabetes Mellitus in individuals using ML techniques. Used various classification and ensemble methods, such as KNN, random forest, logistic regression, and SVM, to achieve high accuracy in diabetes detection. Our system can help in early diagnosis of diabetes and prevent its complications.

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Improving-Machine-Learning-Techniques-for-Diabetic-Prediction

This study aims to develop a system that can predict Type-2 Diabetes Mellitus in individuals using machine learning techniques. We use various classification and ensemble methods, such as k-nearest neighbour, decision tree, random forest, logistic regression, and support vector machine, to achieve high accuracy in diabetes detection. We compare the performance of each algorithm and select the best one for our system. Our system can help in early diagnosis of diabetes and prevent its complications.