An Advance Approach for Diabetes Detection by Implementing Machine Learning Algorithms

Abstract

Early identification of diabetes is vital since it's an incurable condition with no complete cure. We used data mining and machine learning strategies in our investigation to anticipate diabetes. 768 individuals and their relevant attributes are the focus of the hour. Few machine learning methods have been applied to the dataset for the goal of forecasting the occurrence of diabetes. The implemented algorithms' consistency and harshness have been investigated using methods that focused on correlating accuracy and F-1 rankings. Comparison between algorithms is done to increase the accuracy in comparison. We predicted Extra Tree Classifier gives 80% accuracy compared to support vector Machine. The goal of this study is to create a feasible strategy that will aid medical personnel with the diagnosis of diabetic complications at a tender point.

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