Heart_attack_prediction

Heart Attack Prediction

Description

This project is a web-based application designed to predict the likelihood of a heart attack in patients based on various medical attributes. It utilizes a machine learning model to analyze the input data and provide a prediction, accessible through a user-friendly web interface.

Problem Statement

The primary objective of this project is to leverage machine learning to provide a quick and accessible way to assess the risk of a heart attack. By analyzing key medical indicators, the application can assist in early-stage risk assessment, complementing professional medical advice.

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

• Python 3.x
• pip (Python package installer)

Installation

1. Clone the repo:

   git clone https://github.com/your_username/Heart-Attack-Prediction.git
   

2. Navigate to the project directory:

   cd Heart-Attack-Prediction
   

3. Install the required packages:

   pip install -r requirement.txt
   

Running the Application

1. Run the Flask application:

   python application.py
   

2. Open your web browser and go to http://127.0.0.1:5000 to view the application.

Usage

The application provides a web form where you can input the patient's medical data. Fill in the fields and click the "Predict" button to get the heart attack risk prediction.

Dataset

The dataset used for training the model contains 76 attributes, but this project utilizes a subset of 14 key features for prediction. The "target" field indicates the presence of heart disease.

Attribute Information

• age: Age of the patient
• sex: Sex of the patient (1 = male; 0 = female)
• cp: Chest pain type (4 values)
• trestbps: Resting blood pressure (in mm Hg on admission to the hospital)
• chol: Serum cholestoral in mg/dl
• fbs: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
• restecg: Resting electrocardiographic results (values 0, 1, 2)
• thalach: Maximum heart rate achieved
• exang: Exercise induced angina (1 = yes; 0 = no)
• oldpeak: ST depression induced by exercise relative to rest
• slope: The slope of the peak exercise ST segment
• ca: Number of major vessels (0-3) colored by flourosopy
• thal: 0 = normal; 1 = fixed defect; 2 = reversable defect
• target: 0 = less chance of heart attack; 1 = more chance of heart attack

Methodology

The model was developed following these steps:

1. Data Loading and Exploration: The heart.csv dataset was loaded, and an initial exploration was performed to check for missing values and understand the data types.

2. Exploratory Data Analysis (EDA): Visualizations such as count plots, pie charts, pair plots, and a correlation heatmap were used to understand the distribution of data and the relationships between different attributes.

3. Data Preprocessing: The dataset was split into training (70%) and testing (30%) sets. The features were then scaled using StandardScaler to ensure that all features contribute equally to the model's performance.

4. Model Training and Hyperparameter Tuning: A Logistic Regression model was trained on the preprocessed data. GridSearchCV was used to find the optimal hyperparameters for the model, resulting in the best parameters being {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}.

5. Model Evaluation: The model's performance was evaluated on the test set, achieving an accuracy of approximately 81.3%. The classification report and confusion matrix below provide a more detailed breakdown of the model's performance:

   Classification Report:

                 precision    recall  f1-score   support
   
              0       0.78      0.80      0.79        40
              1       0.84      0.82      0.83        51
   
       accuracy                           0.81        91
      macro avg       0.81      0.81      0.81        91
   weighted avg       0.81      0.81      0.81        91
   

   Confusion Matrix:

   [[32  8]
    [ 9 42]]
   

Model

This project uses a Logistic Regression model. The trained GridSearchCV object, which includes the best estimator, is saved as ridge.pkl and is loaded by the Flask application to make predictions.

Visualization

Here is a scatter plot visualizing the relationship between age and maximum heart rate, colored by the target variable (heart attack risk).

Additional Visualizations

In addition to the scatter plot, the application.ipynb notebook in the Notebbook directory contains several other visualizations that provide deeper insights into the dataset:

• Target Class Distribution: A bar chart showing the distribution of patients with and without a high chance of a heart attack.
• Gender Distribution: A pie chart illustrating the gender distribution within the dataset.
• Correlation Heatmap: A heatmap that displays the correlation between all the attributes in the dataset, helping to identify significant relationships.

These visualizations are crucial for understanding the data and the model's behavior. For a detailed view, please refer to the notebook.

Directory Structure

.Heart-Attack-Prediction/
├── Datasets/
│   └── heart.csv
├── Models/
│   ├── ridge.pkl
│   └── scaler.pkl
├── Notebbook/
│   └── ...
├── templates/
│   ├── index.html
│   └── home.html
├── Visualization Graph/
│   └── Scatter Plot.png
├── application.py
├── requirement.txt
└── README.md

Dependencies

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.