Build an AI system to predict menopause symptoms using biomarkers, wearables, and lifestyle data for personalized women's health.
Menopause affects every woman, yet its timing and symptom severity remain unpredictable. Symptoms such as hot flashes, mood swings, and sleep disturbances can drastically affect quality of life and productivity.
Current approaches rely heavily on self-reported symptoms and trial-and-error treatments, which delay interventions and increase discomfort.
A data-driven approach using biomarkers, lifestyle indicators, and wearable data could enable early prediction and personalized symptom management, empowering women and healthcare providers to act proactively rather than reactively.
Mission: Develop a prototype that uses biological, hormonal, and lifestyle data to predict menopause symptom patterns and suggest evidence-based management interventions.
Build a machine learning model to forecast menopause onset or symptom severity. Create a simple web or mobile interface for users to input or sync data from wearables. Provide personalized insights and actionable health management suggestions.
Demonstrate ethical handling of sensitive health data.
Collect and clean sample data, build predictive models for menopause timing and symptom severity.
Build user interface to allow symptom input and display predictions with management suggestions.
Map biomarkers to symptoms and suggest evidence-based interventions.
Use SWAN dataset or generate synthetic health records. Include age, BMI, hormone levels (FSH, LH, estrogen), sleep quality, stress, activity data from wearables, and menstrual history.
Train ML model to predict time to menopause or symptom severity. Identify risk factors that make symptoms worse.
Build dashboard showing predicted symptom timeline, personalized management plan, and alert system for upcoming phases.
Present model accuracy, feature importance, sample user flow. Discuss ethical considerations, data privacy, and scalability.
Each criterion carries 25% weighting:
Quality, accuracy, and efficiency of model and system design
Novelty in approach, algorithm selection, or data utilization
Ease of use, interface design, inclusivity, visualization clarity
Real-world feasibility, potential adoption, contribution to women's health
• Working prototype (mobile/web/API demo)
• Documentation (data sources, algorithm, ethics)
• Pitch deck (max 10 slides)
• Demo video (3-5 minutes)
• Python, R, or JavaScript
• TensorFlow / PyTorch for ML
• Flask/FastAPI backend
• React/Streamlit frontend
Advance predictive health models for women's midlife transitions
Enable personalized, preventive menopause care
Empower women with data-driven health insights
Framework for integrating biomarkers and wearable data
Ready to revolutionize women's health? Build the menopause prediction system of the future.