SymptoCare is an #AI-powered health assistant that provides #symptom-based #risk predictions and mental wellness support using GPT/Gemini. Built with #React, #Flask, s#cikit-learn, and #Firebase, it offers smart insights and doctor booking features.

Health Prediction Web application using Django, machine learning .

This project is Django based Online Health Prediction. This platform utilizes machine learning models to provide online predictions for various health conditions, including mental disorders, polycystic ovary syndrome (PCOS), and obesity.

A health prediction system that takes facial images as input and predicts whether the person in the image is healthy or ill with fever, sore throat or running nose. 16 experiments combining LBP, PCA, LDA, Gabor filter feature extraction methods and SVM, NN, KNN, RF classifiers were run to find the best overall model for the health prediction sys…

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Deployment-ready AI system health prediction engine using ONNX for real-time monitoring and seamless integration with hardware and embedded systems.

Production-ready AI apps: Health ML, Image Classification & NLP — Dockerized & scalable

End-to-end AI system health monitoring solution with data generation, feature engineering, model training, anomaly detection, and dynamic dashboard visualization.

End-to-end cardiovascular risk assessment system using statistical analysis and machine learning. Includes EDA, hypothesis testing, feature engineering, recall-focused modeling, interpretable results, and a Streamlit web app for probability-based risk stratification and decision support.

🎤 ML analysis of voice/audio features to predict health status. Explores spectral patterns, MFCCs, and gender-specific voice characteristics.

This project predicts the likelihood of cardiovascular disease based on patient health data.

Foundation model for health trajectory prediction — architecture shootout across Transformer, Mamba, and Continuous-Time

Pulse of Prevention is an exploratory data analysis (EDA) project focused on understanding the factors contributing to heart disease.

Machine Learning Based Body Fat Percentage Prediction: Developing an accurate and accessible model using anthropometric measurements. This project focuses on practical model development and implementation to improve estimation methods over traditional approaches.

This project predicts the likelihood of sleep disorders based on various health and lifestyle factors.