Chronic Disease Prediction Using Machine Learning

The primary aspiration of this project is to enhance the precision of chronic illness prediction through the integration of Flask and Python ensemble learning techniques, including XGBoosting, gradient boosting, and random forest bagging. By leveraging these sophisticated models within the user-friendly framework of Flask, the system facilitates accurate predictions for a diverse array of chronic conditions based on specific input factors. The system’s overall efficiency and scalability are improved by the inclusion of Python pickling, a method that simplifies the storing and retrieval of learnt models. The goal of the study is to identify the ensemble techniques’ most accurate model in order to produce reliable and solid forecasts. In future, as the system intends to cover a wider range of chronic illnesses, it will be in a good position to emphasize how important early detection is in reducing death rates. Scalability and modular design of the project enable flexibility in integrating additional diseases, resulting in a powerful and all-encompassing predictive analytics tool. The combined strength of Flask and ensemble learning techniques marks a significant advancement in the field of chronic disease prediction.