Heart-Disease-Classification

Predicting heart disease using machine learning

This notebook contains the information I learned from the course:
Course Link

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** Project Structure**

Heart-Disease-Classification/
│
├── App/
│   ├── app.py                      # Main application (UI / API / interface)
│   └── connecter.py                # Handles database or model connections
│
├── Dataset/
│   └── heart-disease.csv           # Main dataset file
│
├── Model/
│   └── logistic_regression_model.pkl  # Saved trained model
│
├── Notebook/
│   └── Heart-Disease-Classification.ipynb  # Jupyter notebook for analysis
│
├── README.md                       # Project overview and usage instructions
└── requirements.txt                 # Dependencies list

The Approach we will be following :

|-- 1. Problem definition
|-- 2. Data
|-- 3. Evaluation
|-- 4. Features
|-- 5. Modelling
|-- 6. Experimentation

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1. Problem Definition

"Given clinical parameters about a patient, can we predict whether or not they have heart disease?"

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2. Data

The original data came from the Cleveland dataset from the UCI Machine Learning Repository.
UCI Repository Link

There is also a version of it available on Kaggle:
Kaggle Dataset Link

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3. Evaluation

Since this is related to the health sector, we need high accuracy.
If we achieve 95% accuracy, we will consider the effort worth it (maybe or maybe not).

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4. Features

1. age - age in years

2. sex - (1 = male; 0 = female)

3. cp - chest pain type

   • 0: Typical angina → chest pain related to decreased blood supply to the heart
   • 1: Atypical angina → chest pain not related to the heart
   • 2: Non-anginal pain → typically esophageal spasms (non-heart related)
   • 3: Asymptomatic → chest pain not showing signs of disease

4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)

   • Anything above 130–140 is typically cause for concern

5. chol - serum cholesterol in mg/dl

   • serum = LDL + HDL + 0.2 * triglycerides
   • Above 200 is cause for concern

6. fbs - fasting blood sugar > 120 mg/dl (1 = true; 0 = false)

   • '>126' mg/dL signals diabetes

7. restecg - resting electrocardiographic results

   • 0: Nothing to note
   • 1: ST-T Wave abnormality
     • Can range from mild symptoms to severe problems
     • Signals non-normal heartbeat
   • 2: Possible or definite left ventricular hypertrophy
     • Enlarged heart's main pumping chamber

8. thalach - maximum heart rate achieved

9. exang - exercise induced angina (1 = yes; 0 = no)

10. oldpeak - ST depression induced by exercise relative to rest

    • Looks at stress of heart during exercise
    • Unhealthy heart will stress more

11. slope - the slope of the peak exercise ST segment

    • 0: Upsloping → better heart rate with exercise (uncommon)
    • 1: Flatsloping → minimal change (typical healthy heart)
    • 2: Downsloping → signs of unhealthy heart

12. ca - number of major vessels (0–3) colored by fluoroscopy

    • Colored vessel means the doctor can see the blood passing through
    • The more blood movement, the better (no clots)

13. thal - thalium stress result

    • 1, 3: Normal
    • 6: Fixed defect → used to be defect but okay now
    • 7: Reversible defect → no proper blood movement when exercising

14. target - whether the patient has disease or not

    • 1 = Yes
    • 0 = No
    • (Predicted attribute)

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How to Use the App

Step 1: Clone the Repository

• Run the following command in your terminal

git clone https://github.com/Skanda02/Heart-Disease-Classification.git

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Step 2: Create a Virtual Environment

• On macOS / Linux:

python3 -m venv .venv

• On Windows:

python -m venv .venv

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Step 3: Activate the Virtual Environment and Install Dependencies

Activate the environment:

• macOS / Linux:

source .venv/bin/activate

• Windows:

.venv\Scripts\activate

Install the required packages:

pip install -r requirements.txt

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Step 4: Run the App

Start the application by running:

• macOS / Linux

python3 App/app.py

• Windows

python App/app.py

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