K-Means and Decision Tree Approach to Predict the Severity of Diabetes Cases

The prevalence of type 2 diabetes is fairly high worldwide. Early identification is essential for Diabetic management and prophylaxis. Due to their capacity to classify data, Machine Learning (ML) techniques are currently becoming more and more important in the field of diagnostic purposes. To aid in the diagnosis of Type 2 diabetes, a mixed proposed methodology is put out in this work. K-means has been employed in the proposed model-based minimization and a J48 decision tree is used as a classifier to classify. We were using the Pima Indians Diabetic Database from the UCI ML Repository to obtain the results of the experiment. The outcome demonstrates that, in comparison to other earlier research that is described in the literature, the proposed model has achieved more reliability. These findings demonstrate that the proposed methodology might be useful for diagnosing Type 2 diabetes.