Predicting Type 2 Diabetes Using Machine Learning and Flask Web Framework

Type 2 diabetes, considered a serious global health problem, is known for its broad prevalence and severe consequences. The vital requirement for early detection and response to limit its effect has been underlined. This study has applied machine learning approaches, notably ensemble methods, attaining an outstanding accuracy rate of 95.56% in predicting Type 2 diabetes. Through the employment of Flask, a web framework, a user-friendly web application was constructed to boost accessibility and usability. Machine learning's key importance resides in its power to evaluate huge information, allowing accurate illness prediction. The study serves as a testimony to the promise of machine learning in improving healthcare by allowing early diagnosis, a vital part of good illness treatment. The Flask-based interface has been intended to enable smooth user interaction, enabling users to simply estimate their risk of diabetes type 2. The end conclusion is a unique, accurate, and simply accessible tool for early diabetes type 2 prediction.