An AI-powered web platform that analyzes lifestyle habits, health history, and symptoms to predict potential health risks early and generate personalized daily diet plans and health recommendations.
This system focuses on preventive healthcare, helping users improve their lifestyle before diseases develop.
Many health conditions such as diabetes, heart disease, obesity, and sleep disorders develop slowly due to unhealthy lifestyle habits. Most people detect these problems only after symptoms become severe.
Existing fitness apps mainly track activity but do not provide intelligent health predictions or personalized preventive insights.
This project aims to build an AI-powered health monitoring system that can:
- Predict possible disease risks
- Analyze symptoms
- Monitor daily health habits
- Generate personalized diet plans
- Track improvement over time
The goal of this project is to build a Preventive Health Intelligence Platform that can:
- Predict disease risks using machine learning
- Analyze symptoms and detect possible health issues
- Generate personalized daily diet plans
- Track daily health habits
- Provide health improvement insights
- Encourage healthy lifestyle habits through streak tracking
- Visualize health trends through charts and analytics
Secure user account system.
Features:
- User Signup
- User Login
- Password Encryption
- JWT Authentication
Users create a complete health profile including:
- Age
- Gender
- Height
- Weight
- BMI
- Blood Group
The system collects detailed health data including:
- Sleep duration
- Exercise frequency
- Water intake
- Stress levels
- Screen time
- Smoking habits
- Alcohol consumption
- Veg / Non-Veg
- Junk food frequency
- Sugar intake
- Oil consumption
- Meal timings
The platform records:
- Existing diseases
- Medications
- Previous surgeries
- Allergies
Family health history helps improve ML prediction accuracy.
Example conditions tracked:
- Diabetes
- Heart disease
- Hypertension
- Cancer
- Thyroid disorders
Users log their daily health activities.
Daily logs include:
- Sleep hours
- Meals
- Exercise
- Water intake
- Mood
- Symptoms
The platform uses Machine Learning models to predict potential health risks.
Supported predictions:
- Diabetes Risk
- Heart Disease Risk
- Obesity Risk
- Sleep Disorder Risk
Models analyze:
- BMI
- lifestyle habits
- food patterns
- symptoms
- family history
Users can select symptoms such as:
- Fever
- Headache
- Chest pain
- Fatigue
- Dizziness
- Breathing issues
The AI system predicts possible medical conditions with probability scores.
Example output: Possible Conditions:
Flu – 65% Viral Infection – 22% Stress Fatigue – 13%
The system generates daily personalized diet plans based on:
- Age
- BMI
- Health conditions
- Predicted disease risks
- Lifestyle habits
Example:
Breakfast Oats + boiled eggs + green tea
Lunch Brown rice + grilled chicken + vegetables
Dinner Vegetable soup + whole wheat roti
Foods to Avoid Sugary drinks, fried food, processed snacks
A Health Score Algorithm evaluates user lifestyle daily.
Scoring factors:
- Sleep quality
- Diet quality
- Exercise level
- Symptoms
Example: Sleep Score: 20/25 Diet Score: 18/25 Exercise Score: 15/25 Symptoms Score: 22/25
Total Health Score: 75/100
To encourage healthy habits, the system tracks daily streaks.
Examples:
- Sleep streak
- Exercise streak
- Healthy eating streak
- Hydration streak
The dashboard visualizes health trends using charts.
Includes:
- Weight progress
- Sleep patterns
- Health score trends
- Disease risk predictions
- Calorie intake tracking
Users receive automated reminders.
Examples:
Morning reminder
"Log your breakfast and water intake."
Evening reminder
"Update your sleep and symptoms."
Weekly health report.
The system uses ML models trained on health datasets.
Dataset: PIMA Indians Diabetes Dataset
Model: Random Forest / Logistic Regression
Dataset: UCI Heart Disease Dataset
Model: Random Forest / Gradient Boosting
Features:
- BMI
- diet habits
- physical activity
Model trained on symptom–disease datasets.
React Frontend │ ▼ Node.js Backend (Express API) │ ▼ MongoDB Database │ ▼ Python ML Prediction Service │ ▼ Prediction Results → Frontend Dashboard
Collections used:
- Users
- HealthProfiles
- FoodHabits
- MedicalHistory
- FamilyHistory
- DailyLogs
- Predictions
- DietPlans
- HealthScores
- React.js
- Tailwind CSS
- Chart.js
- Node.js
- Express.js
- MongoDB
- Python
- Scikit-learn
- TensorFlow
- JWT
- bcrypt