An AI-driven healthcare assistant that predicts diseases based on user-provided symptoms. This project leverages machine learning for disease prediction, provides descriptions and precautionary measures, and includes a user-friendly GUI with text-to-speech integration.

VAERS Adverse Event Analysis for COVID 19 Vaccine : A hybrid approach combining LLMs (Gemini 1.5 Flash) and statistical methods for enhanced vaccine safety signal detection. Analyzes temporal and associative relationships in VAERS symptom data.

🏥 AI-powered medical report generation with RAG, HIPAA compliance, and Next.js frontend

AI-driven disease prediction model based on symptoms using machine learning.

A comprehensive first aid and healthcare mobile app built with Flutter. Features include emergency contact management, first aid instructions, illness database, hospital locator, training modules, health journaling, symptom analysis, and community support. Uses Firebase for backend services and includes offline capabilities.

To investigate brain network dynamics linked with dimensionally-based symptom profiles exhibited across a transdiagnostic cohort of participants with and without psychiatric diagnoses.

Machine learning–based disease prediction system that identifies the most likely illness from input symptoms and provides relevant medication suggestions. Built with Python, scikit-learn, and Flask as part of a graduate research project at Montclair State University

SVM-based COVID-19 prediction model with cross-validation and full performance evaluation (precision, recall, F1-score, confusion matrix).