A Machine Learning Approach for Multiclass Classification of Diabetes
Keywords:
Diabetes, Machine Learning, Logistic Regression, SVM, Decision Tree, Random Forest, Gradient BoostingAbstract
Diabetes is an increasing global health issue, with millions at risk due to factors like lifestyle, genetics, and other health conditions. Early diagnosis is essential for timely treatment, avoiding complications, and easing the strain on healthcare systems. The disease’s complexity, with its different stages, requires advanced models that can distinguish between diabetic, non-diabetic, and pre-diabetic individuals. This study aimed to develop a precise multiclass classification model to predict a patient’s diabetes status based on various health indicators. In addition to standard factors like blood sugar level, BMI, cholesterol, and age, external risk factors have also been considered for better accuracy. In the current study, the target variable categorizes patients as Diabetic, Non-Diabetic, or Pre-Diabetic. The current work applies Logistic Regression, SVM, Decision Tree, Random Forest, and Gradient Boosting models to address the classification challenge. After training and testing the models, Random Forest has been identified to deliver the highest accuracy at 98%, outperforming the others. These findings highlight the power of machine learning in effectively classifying patients based on diabetes status.
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