Machine Learning-Based Prediction of Type 2 Diabetes in Indian Population

This study presents a machine learning-based approach for the prediction of Type 2 Diabetes within the Indian population. With the rapid increase in diabetes prevalence across India, early and accurate detection has become a critical healthcare priority. The objective of this research is to develop and evaluate predictive models that can assist in identifying individuals at risk. The dataset used in this study consists of key medical attributes such as glucose level, blood pressure, BMI, age, and insulin levels. Multiple classification algorithms, including Decision Tree, Random Forest, and Logistic Regression, were implemented to analyze the data and generate predictions. The performance of each model was evaluated using standard metrics such as accuracy, precision, and recall. Among the applied models, ensemble methods demonstrated improved predictive performance, highlighting their effectiveness in handling complex healthcare datasets. The results of this study emphasize the potential of machine learning techniques in supporting early diagnosis and decision-making in the healthcare sector. This research contributes to the growing field of AI-driven healthcare solutions, particularly in the context of the Indian population, where diabetes poses a significant public health challenge.