Heart Disease Prediction Using AdaBoost and Flask

Overview

This project is a web-based application that predicts the likelihood of heart disease using the AdaBoost Machine Learning Algorithm. The system is built using Python, Flask, HTML, and CSS, integrating machine learning to provide accurate predictions based on input medical data.

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Features

• Predict heart disease risk using patient data.
• User-friendly interface created with HTML and CSS.
• Machine learning backend powered by AdaBoost algorithm.
• Lightweight and fast API using Flask.
• Scalable and easy-to-deploy solution.

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Technology Stack

Frontend

• HTML: For structuring the web pages.
• CSS: For designing and styling the interface.

Backend

• Python: Core programming language for implementing logic.
• Flask: Lightweight framework for building the web application.

Machine Learning

• AdaBoost Algorithm: A powerful ensemble learning algorithm for heart disease prediction.
• Scikit-learn: For implementing and training the ML model.

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Workflow

1. User inputs medical parameters (e.g., age, cholesterol, blood pressure, etc.) into the web form.
2. The data is sent to the backend through the Flask API.
3. The trained AdaBoost model processes the input and predicts the likelihood of heart disease.
4. The prediction result is displayed on the web page.

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Installation

Follow these steps to set up and run the project locally:

Prerequisites

• Python 3.8 or above
• pip install -r requirements.txt

Steps

1. Clone the Repository:

   git clone https://github.com/Akhil1409906/heart-disease-prediction.git
   cd heart-disease-prediction