❤️ Heart Disease Prediction Web Application ❤️

📖 Overview

The Heart Disease Prediction Web Application helps users predict their risk of heart disease using machine learning models. The app is built using Flask, SQLAlchemy, Bcrypt, and is deployed with Docker. It offers a user-friendly interface for inputting health data, viewing prediction results, and managing profiles.

🚀 Access the App

Access the live version: http://188.121.99.111:5000

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✅ Features

1. User Registration and Authentication

   • Secure registration and login functionality.
   • Password hashing for added security using Bcrypt.

2. User Profile and Prediction History

   • View personalized prediction history.
   • Option to download past predictions as reports.

3. Real-Time Predictions

   • Immediate prediction results based on user-inputted health data.

4. Responsive Design

   • Optimized for both desktop and mobile users.

5. Error Handling

   • Clear error messages and feedback for incorrect inputs.
   • Flash messages for successful actions like registration and login.

6. Data Security and Privacy

   • Secure storage of user data using an SQLite database.
   • Encrypted passwords for user accounts.

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🎯 Objective

The project's goal is to collaboratively develop a web application that:

• Utilizes machine learning for heart disease prediction.
• Implements user registration and authentication systems using a database.
• Containerizes the application with Docker and deploys it using Docker Swarm on a cloud-based host for scalability.

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✅ Project Structure

Heart Disease Prediction Web Application
│   ├── assets
│   │   ├── dataset
│   │   ├── images
│   │   ├── heart_disease_diagnosis_model.ipynb
│   │   ├── heart_disease_diagnosis_model.py
│   ├── instance 
│   │   ├── Heart_Disease_Diagnosis.db
│   ├── static 
│   │   ├── css
│   │   │  └── style.css
│   │   ├── images
│   │   │  ├── Error_401.webp
│   │   │  ├── Error_403.webp
│   │   │  ├── Error_404.webp
│   │   │  ├── Error_500.webp
│   │   │  ├── Heart_Disease.webp
│   │   │  ├── No_Disease.webp
│   │   │  ├── Logo.webp
│   │   │  ├── smart_app.webp
│   ├── templates/
│   │   ├── base_error.html
│   │   ├── base.html
│   │   ├── error_401.html
│   │   ├── error_403.html
│   │   ├── error_404.html
│   │   ├── error_500.html
│   │   ├── history.html
│   │   ├── home.html
│   │   ├── input.html
│   │   ├── login.html
│   │   ├── profile.html
│   │   ├── register.html
│   │   └── result.html
│   ├── .gitignore
│   ├── app.py
│   ├── form.py
│   ├── heart_disease_diagnosis_model.pkl
│   ├── members.txt
│   ├── model.py 
│   ├── README.md
│   ├── requirements.txt
│   ├── Dockerfile
    └── docker-compose.yml

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Dataset and Model

Dataset Details

The dataset used for training the model is sourced from Kaggle's Heart Disease Dataset. It includes 303 observations with 14 features, such as:

• Age: Patient's age
• Sex: Gender (1 = Male, 0 = Female)
• Chest Pain Type:
  • 0 = Typical angina
  • 1 = Atypical angina
  • 2 = Non-anginal pain
  • 3 = Asymptomatic
• Resting Blood Pressure: Measurement in mm Hg
• Cholesterol: Serum cholesterol in mg/dl
• Fasting Blood Sugar: >120 mg/dl (1 = True, 0 = False)
• Resting ECG: Electrocardiographic results
• Max Heart Rate: Maximum heart rate achieved
• Exercise-Induced Angina: (1 = Yes, 0 = No)
• Old Peak: ST depression induced by exercise relative to rest
• ST Slope: Slope of the peak exercise ST segment
• Number of Major Vessels: (0-3) colored by fluoroscopy
• Thallium Stress Test Results: (1 = Normal, 2 = Fixed Defect, 3 = Reversible Defect)
• Target: Presence of heart disease (1 = Disease, 0 = No Disease)

Data Preprocessing and Feature Engineering

• Renamed columns for better readability.
• Categorical Mapping for variables:
  • Sex mapped to "Male" or "Female".
  • Chest Pain categorized into descriptive labels.
  • Resting ECG, Exercise-Induced Angina, and Thallium levels mapped to categorical descriptions.
• Scaling: StandardScaler was applied to normalize features.

Exploratory Data Analysis (EDA)

• Univariate Analysis: Histograms and box plots for distribution analysis.
• Insights:
  • Most patients were aged around 54.
  • Majority had typical chest pain.
  • Significant correlation found between chest pain type and heart disease presence.

Model Building

We trained a K-Nearest Neighbors (KNN) classifier using scikit-learn. This simple yet effective model classifies patients based on their proximity to similar cases in the dataset.KNN is a simple, yet powerful algorithm that classifies data points based on their proximity to the nearest neighbors in the feature space.

• Data Splitting: 80% training, 20% testing.
• Hyperparameter Tuning: Used GridSearchCV to optimize the number of neighbors.
• Model Accuracy: Achieved an accuracy of ~85%.

Model Evaluation

• Confusion Matrix: Used to visualize the model's performance.
• Classification Report: Displays precision, recall, and F1-score for both classes (Heart Disease vs. No Disease).
• Model Saving: The trained model was saved using pickle for deployment.

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🛢️ Database Schema and Models

Tables

1. Users Table

   • Stores user information and authentication details.
   • Fields:
     • id: Unique identifier (Primary Key)
     • username: Unique username
     • email: Unique email address
     • password_hash: Hashed password
   • Relationship: One-to-many relationship with prediction_history.

2. PredictionHistory Table

   • Stores predictions made by each user.
   • Fields:
     • id: Unique identifier (Primary Key)
     • user_id: Foreign key referencing the users table
     • age, sex, chest_pain, resting_blood_pressure, cholesterol, etc.: User inputs for predictions
     • result: Prediction outcome ("Positive" or "Negative")
     • timestamp: Time when the prediction was made

Model Classes

# User Model
class User(UserMixin, db.Model):
    __tablename__ = 'users'
    id = db.Column(db.Integer, primary_key=True)
    username = db.Column(db.String(80), unique=True, nullable=False)
    email = db.Column(db.String(120), unique=True, nullable=False)
    password_hash = db.Column(db.String(200), nullable=False)
    predictions = db.relationship('PredictionHistory', backref='user', lazy=True)

    def set_password(self, password):
        self.password_hash = bcrypt.generate_password_hash(password).decode('utf-8')

    def check_password(self, password):
        return bcrypt.check_password_hash(self.password_hash, password)

# PredictionHistory Model
class PredictionHistory(db.Model):
    __tablename__ = 'prediction_history'
    id = db.Column(db.Integer, primary_key=True)
    user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)
    age = db.Column(db.Integer, nullable=False)
    sex = db.Column(db.String, nullable=False)
    chest_pain = db.Column(db.String, nullable=False)
    # Other fields here...
    result = db.Column(db.String, nullable=False)
    timestamp = db.Column(db.DateTime, default=datetime.utcnow)

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Requirements

• Python 3.x
• Flask
• SQLAlchemy
• Flask-Login
• Bcrypt
• Scikit-learn
• Docker
• Docker Compose

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Installation Instructions

Prerequisites

• Python 3.x
• Docker and Docker Compose

👉 Step 1: Clone the Repository

git clone https://github.com/Ulduzpp/Smart_Web_Nexus.git
cd Smart_Web_Nexus

👉 Step 2: Setup Virtual Environment

python3 -m venv venv
source venv/bin/activate

👉 Step 3: Install Dependencies

pip install -r requirements.txt

👉 Step 4: Run the Application(locally)

python app.py

• Open your browser and navigate to: http://127.0.0.1:5000

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🐳 Docker Deployment

Build and Run Docker Container

1. Build the Docker Image

   docker build -t smart_web_nexus .

2. Run the Docker Container

   docker run -p 5000:5000 smart_web_nexus

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Team Members

Role	Name
Frontend Developer	Fatemeh Habibimoghaddam
Backend Developer	Ouldouz Pakpour
Database Engineer	Sahar Abdi
Machine Learning Engineer	Hanie Fazli
Docker & MLOps Engineers	Touba Derakhshandeh & Hanie Fazli

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Contributing

We welcome contributions to improve this project! Please fork the repository and submit a pull request.

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Demo

Heart_Disease_Prediction.mp4

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