https://github.com/gracysapra/heart-disease-prediction-project

This project implements various machine learning models, including Logistic Regression, Random Forest, and FNN, to predict heart disease. Utilizing Gradio for a user-friendly interface and MLflow for model management, it aims to enhance diagnostic accuracy.
https://github.com/gracysapra/heart-disease-prediction-project

classification dataset decision-trees deep-learning ensemble-learning feedforward-neural-network fnn gradio heart-disease-prediction logistic-regression machine-learning mlflow random-forest svm user-friendly-interface xgboost

Last synced: 3 days ago
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This project implements various machine learning models, including Logistic Regression, Random Forest, and FNN, to predict heart disease. Utilizing Gradio for a user-friendly interface and MLflow for model management, it aims to enhance diagnostic accuracy.

• Host: GitHub
• URL: https://github.com/gracysapra/heart-disease-prediction-project
• Owner: Gracysapra
• Created: 2024-10-22T06:26:16.000Z (23 days ago)
• Default Branch: main
• Last Pushed: 2024-10-22T06:30:31.000Z (23 days ago)
• Last Synced: 2024-10-23T09:15:43.575Z (22 days ago)
• Topics: classification, dataset, decision-trees, deep-learning, ensemble-learning, feedforward-neural-network, fnn, gradio, heart-disease-prediction, logistic-regression, machine-learning, mlflow, random-forest, svm, user-friendly-interface, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 268 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Heart Disease Prediction Project

## Overview

This project aims to develop a predictive model for heart disease using various machine learning algorithms. By leveraging patient data, the model assesses the likelihood of heart disease, providing a valuable tool for healthcare professionals to assist in early diagnosis and treatment.

## Dataset

The dataset utilized for this project consists of 1,025 records with 14 attributes, including:

- **Age**: Age of the patient

- **Sex**: Gender of the patient

- **Chest Pain Type (cp)**: Type of chest pain experienced

- **Resting Blood Pressure (trestbps)**: Blood pressure measurement

- **Cholesterol Level (chol)**: Serum cholesterol in mg/dl

- **Fasting Blood Sugar (fbs)**: Blood sugar level

- **Resting Electrocardiographic Results (restecg)**: Electrocardiographic results

- **Maximum Heart Rate Achieved (thalach)**: Maximum heart rate achieved during exercise

- **Exercise Induced Angina (exang)**: Indication of angina during exercise

- **Old Peak**: ST depression induced by exercise relative to rest

- **Slope**: Slope of the peak exercise ST segment

- **Number of Major Vessels (ca)**: Number of major vessels colored by fluoroscopy

- **Thalassemia (thal)**: Thalassemia status

- **Target**: Presence (1) or absence (0) of heart disease

## Machine Learning Models

The project implements several machine learning models to predict heart disease, including:

- **Logistic Regression**

- **Random Forest**

- **Decision Trees**

- **Support Vector Machines (SVM)**

- **XG Boost**

- **Feedforward Neural Networks (FNN)**

  

Each model is trained and evaluated based on accuracy, precision, recall, and F1 score. The results are presented using confusion matrices and classification reports to provide a comprehensive understanding of model performance.

## User Interface

To enhance user experience, the project incorporates **Gradio**, creating an interactive web interface where users can input patient data and receive immediate predictions. This feature allows healthcare professionals to utilize the tool effectively in clinical settings.

## Model Management

The project integrates **MLflow** for streamlined model management. MLflow is used for:

- Logging experiments and tracking model parameters

- Storing and versioning trained models

- Comparing model performance metrics over time

This facilitates the selection of the best-performing model for deployment and future reference.

## Conclusion

The heart disease prediction project combines advanced machine learning techniques with practical user interface design and effective model management strategies. By providing accurate predictive analytics, this tool aims to assist healthcare providers in making informed decisions, ultimately improving patient outcomes.

## Installation

To run the project locally, follow these steps:

1. Clone the repository:

   ```bash

   git clone https://github.com/yourusername/heart-disease-prediction.git

   ```

2. Navigate to the project directory:

   ```bash

   cd heart-disease-prediction

   ```

3. Install the required packages:

   ```bash

   pip install -r requirements.txt

   ```

4. Run the Gradio interface:

   ```bash

   python app.py

   ```

## Usage

Open a web browser and navigate to `http://localhost:7860` to access the Gradio interface. Input the necessary patient data to receive predictions regarding heart disease.