Diabetes Prediction Using Decision Tree Classifier

Diabetes mellitus is a persistent metabolic disorder characterized by elevated blood glucose levels, presenting substantial risks to overall health if left undiagnosed or untreated. Early and accurate detection is paramount for effective disease management and prevention of complications. This study leverages a machine learning-based approach, specifically a Decision Tree Classifier, to predict diabetes using clinical parameters such as cholesterol levels, glucose stability, body metrics, and blood pressure. The dataset undergoes rigorous preprocessing, including missing value handling and categorical encoding, ensuring robust model training. Performance evaluation is conducted using key metrics, including accuracy, precision, recall, specificity, and F1-score. The proposed model achieves an impressive accuracy of 90.9µ, demonstrating its efficacy in diabetes prediction. The Decision Tree algorithm is chosen for its interpretability and computational efficiency, making it a suitable tool for medical diagnostics. While the model exhibits strong predictive capabilities, further enhancements such as hyperparameter tuning and ensemble learning could bolster its generalization to diverse patient populations. Future research will focus on integrating real-time patient data and exploring deep learning techniques for improved accuracy. This study emphasizes the capability of machine learning to revolutionize early disease detection and propel the development of predictive healthcare solutions.