Heart Disease Prediction

Project Overview

This project aims to predict heart disease using various health indicators. Early detection can potentially save lives. We are a team of 4 AI enthusiasts participating in the AI LAUNCH LAB R&D Programs of Winter 2024.

Team Members

• Sohaib Kaidali
• Anton
• Radman
• Agam

Problem Statement

Heart disease is the leading cause of death globally. Early detection can potentially save lives. We aim to build a machine learning model that can predict the likelihood of heart disease based on various health indicators, contributing to improved healthcare outcomes.

Project Workflow

Step 1: Data Collection and Preprocessing

1. Collect Data

   • Download the dataset from Kaggle.
   • Explore the dataset to understand its structure and features.
   • Check for missing values and handle them appropriately.

2. Data Preprocessing

   • Normalize or scale numerical features.
   • Split the dataset into training and testing sets.

Step 2: Exploratory Data Analysis (EDA)

1. Visualize Data

   • Create histograms, scatter plots, and box plots to explore the distribution of features.
   • Generate correlation matrices to identify relationships between variables.
   • Analyze trends and patterns in the data.

2. Feature Engineering

   • Select relevant features for modeling.
   • Perform dimensionality reduction if necessary.

Step 3: Model Building and Evaluation

1. Model Selection

   • Choose appropriate machine learning algorithms for the task.
   • Train baseline models with default parameters.

2. Model Evaluation

   • Evaluate models using cross-validation and appropriate metrics.
   • Tune hyperparameters to improve model performance.

Step 4: Model Deployment and Documentation

1. Deploy Model

   • Deploy the trained model to a web service or application.

2. Documentation

   • Document the project's methodology, findings, and conclusions in a report.
   • Write README files with instructions for running the code and reproducing results.

Dataset

The dataset used in this project is sourced from Kaggle and can be found here. It contains 253,680 records and 21 features.

Features description

• HighBP : Indicates if the person has been told by a health professional that they have High Blood Pressure.
• HighChol : Indicates if the person has been told by a health professional that they have High Blood Cholesterol.
• CholCheck : Cholesterol Check, if the person has their cholesterol levels checked within the last 5 years.
• BMI : Body Mass Index, calculated by dividing the persons weight (in kilogram) by the square of their height (in meters).
• Smoker : Indicates if the person has smoked at least 100 cigarettes.
• Stroke : Indicates if the person has a history of stroke.
• Diabetes : Indicates if the person has a history of diabetes, or currently in pre-diabetes, or suffers from either type of diabetes.
• PhysActivity : Indicates if the person has some form of physical activity in their day-to-day routine.
• Fruits : Indicates if the person consumes 1 or more fruit(s) daily.
• Veggies : Indicates if the person consumes 1 or more vegetable(s) daily.
• HvyAlcoholConsump : Indicates if the person has more than 14 drinks per week.
• AnyHealthcare : Indicates if the person has any form of health insurance.
• NoDocbcCost : Indicates if the person wanted to visit a doctor within the past 1 year but couldn’t, due to cost.
• GenHlth : Indicates the persons response to how well is their general health, ranging from 1 (excellent) to 5 (poor).
• Menthlth : Indicates the number of days, within the past 30 days that the person had bad mental health.
• PhysHlth : Indicates the number of days, within the past 30 days that the person had bad physical health.
• DiffWalk : Indicates if the person has difficulty while walking or climbing stairs.
• Sex : Indicates the gender of the person, where 0 is female and 1 is male.
• Age : Indicates the age class of the person, where 1 is 18 years to 24 years up till 13 which is 80 years or older, each interval between has a 5-year increment.
• Education : Indicates the highest year of school completed, with 0 being never attended or kindergarten only and 6 being, having attended 4 years of college or more.
• Income : Indicates the total household income, ranging from 1 (at least $10,000) to 6 ($75,000+)

Getting Started

Google Colab Workflow

1. Open Google Colab:
   • Go to Google Colab and sign in with your Google account if you haven't already.
2. Open a Notebook from GitHub:
   • Click on "File" > "Open notebook" > "GitHub".
   • Check "Include private repos" top right.
   • Enter the URL of your GitHub repository "https://github.com/kaidalisohaib/AICohortW24" in the provided field.
   • Select the notebook you want to open from the repository.
   • The notebook will open in Google Colab.
3. Make Changes to the Notebook:
   • You can make changes to the notebook as needed using the Colab interface.
4. Save a Copy to GitHub:
   • Once you've made your changes, click on "File" > "Save a copy in GitHub".
   • Authenticate with GitHub if prompted by providing your username and password or personal access token.
   • Choose the repository where you want to save the notebook.
   • Choose the correct branch and enter a commit message describing your changes.
   • Click on "OK" to save the notebook to your GitHub repository.
5. Verify Changes on GitHub:
   • Go to your GitHub repository to verify that the changes have been saved successfully.

Local Workflow

1. Setup Environment:
   • Make sure to use Python 3.10.1.
   • Configure a Python environment: Run each command, one after the other.

     python -m venv venv

     .\venv\Scripts\activate

2. Install Dependencies:
   • Install the required Python packages by running:

     pip install -r requirements.txt

3. Make Changes to the Notebook:
   • Make your changes, commit & push them.