| # 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: | |
| ```sh | |
| git clone https://github.com/your_username/Heart-Attack-Prediction.git | |
| ``` | |
| 2. Navigate to the project directory: | |
| ```sh | |
| cd Heart-Attack-Prediction | |
| ``` | |
| 3. Install the required packages: | |
| ```sh | |
| pip install -r requirement.txt | |
| ``` | |
| ### Running the Application | |
| 1. Run the Flask application: | |
| ```sh | |
| 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. | |