AI-Based Health Risk Prediction Using Real-Time Pose Estimation and Deep Learning

Preventive healthcare needs early health risk detection of conditions like obesity, malnutrition, and metabolic disorders. Conventional screening procedures are either expensive, demand trained staff, and physical visits, hence less accessible and scalable. This paper introduces a real-time, AI-powered health risk prediction system that employs computer vision and deep learning to gauge physical health with a webcam alone. The system proposed utilizes MediaPipe and OpenCV to infer body pose landmarks from live video feed. Anthropometric features like height, BMI, shoulder width, arm length, and leg length, as well as waist-to-hip ratio, are automatically computed from these. These values, together with user-supplied information such as age and gender, are input into a deep learning model that has been trained to predict an individual as being in one of four categories: healthy, risk of obesity, malnourishment, or risk of metabolic disorder. All processing is carried out in real time and aggregated into a web-based interface, providing instant feedback, graphical insights, and health suggestions to the user. The backend is implemented with Python, TensorFlow/Keras, and Flask for rapid processing and model inference. This system illustrates the promise of affordable, non-invasive technologies for initial health testing. It can be used in telemedicine, fitness programs, and distant health screening, increasing preventive care to be more scalable and affordable.