Osteoporosis is a condition that weakens bones, increasing the risk of fractures, and often progresses without noticeable symptoms until it reaches advanced stages. Early detection is essential to manage and prevent further complications. This project aims to develop an optimized approach for detecting osteoporosis using machine learning techniques, leveraging lifestyle factors as input data. The system utilizes algorithms such as Gradient Boosting, Voting Classifier, Stacking Classifier, and CatBoost to predict the risk of osteoporosis based on factors like age, exercise habits, diet, and medical history. The dataset used for the project includes various lifestyle-related features known to influence bone health. The system provides an easy-to-use interface where users can input their data and receive a prediction regarding their osteoporosis risk. The back-end of the system is built using Python and Flask, while the front-end is developed using HTML, CSS, and JavaScript, ensuring a user-friendly experience. By utilizing machine learning models, this project offers a non-invasive and cost-effective method for osteoporosis risk detection, providing individuals with an accessible tool to understand their bone health. The project focuses on optimizing model performance and ensuring high accuracy in predicting osteoporosis risk, contributing to early identification and intervention.

Keywords: osteoporosis, machine learning, Gradient Boosting, Voting Classifier, Stacking Classifier, CatBoost, prediction, lifestyle factors, Python, Flask.