Human-Centric Approach to Diabetes Prediction Using Machine Learning Models

Diabetes mellitus is one of the most pressing global health issues, affecting millions worldwide. Early prediction and timely management can significantly reduce the disease's impact and improve the quality of life for individuals at risk. This research presents a detailed and human- centric approach to building a diabetes prediction model using machine learning algorithms. By leveraging real-world patient data, we explore various supervised learning techniques, assess their accuracy, and highlight the importance of interpretability in predictive healthcare. This paper emphasizes the ethical implications, real-world applications, and the need to bridge the gap between technology and patient-centered care.