Heart Disease Prediction using Python & Machine Learning

Posted 2025-08-13 17:00:21

Heart Disease Prediction using Python & Machine Learning is a final-year academic project that uses medical data and ML algorithms to predict the likelihood of heart disease. Built with Python, scikit-learn, NumPy, and joblib, it trains and evaluates models like Logistic Regression and Random Forest to deliver accurate results. The system features SQLite integration, a user-friendly interface, and includes a complete project report, PPT, and source code.

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In recent years, machine learning has revolutionized healthcare by enabling early detection of various ailments. Among these, heart disease stands out as one of the leading causes of death globally. Recognizing this pressing issue, a final-year academic project titled “Heart Disease Prediction using Python & Machine Learning” was developed to assist in timely detection and intervention.

Built by Anuj Kumar, this project integrates cutting-edge ML techniques with a user-friendly web interface, forming a comprehensive prediction system. It’s perfect as a reference or even a starting point for those pursuing academic projects in domains like Data Science, BCA, MCA, or B.Tech.

Key Features

User Input Form: Capture essential medical details — age, blood pressure, cholesterol levels, etc. Php Gurukul
Risk Prediction: Leverages trained ML models to predict heart disease likelihood
Performance Insights: Visual output compares model accuracy (e.g., bar charts)
Admin Panel: Centralized dashboard to manage user data, review prediction history, and export reports
Data Storage: Stores all data securely in SQLite and enables exportable reporting

The Heart Disease Prediction using Python and Machine Learning project by PHPGurukul presents a compelling blend of practicality and academic value. Ideal for those diving into Python-based ML, web development with Django, or healthcare analytics, this project is an excellent reference point for creating robust, user-centered applications.

Final Tech Stack Used

Frontend / Web Interface:

Django (Python Web Framework) — Used to create the web interface for user input, displaying predictions, and managing data
HTML5, CSS3, JavaScript — For rendering and styling web pages
Bootstrap (optional) — For responsive UI components
Django Templates — For dynamic web page rendering

Machine Learning / Backend Logic:

scikit-learn — Machine Learning library used to implement algorithms like Logistic Regression, Decision Tree, Random Forest, KNN
NumPy — For numerical operations and matrix manipulation
Pandas — For handling and preprocessing datasets
joblib — To save and load the trained machine learning model

Database:

SQLite — Lightweight relational database used to store user data and predictions
Django ORM (Object Relational Mapper) — Handles interaction between Django models and the SQLite database

Tools & Environment:

Python 3.x — Core programming language used
PyCharm — IDE for development
Virtualenv / pip — For managing dependencies

Key Features:

Input Form for Patient Medical Details (age, BP, cholesterol, etc.)

Prediction of Heart Disease Risk

Model Accuracy Comparison (Bar Chart Output)

SQLite Integration for Data Storage

Admin Panel to Manage Users and Records

Easy-to-use Interface

Exportable Reports

How to run the Heart Disease Prediction Python ML Project

1. Download the zip file

2. Extract the file, copy heartdisease, folder and paste it on the desktop

3. Open PyCharm and Import the project in pycharm

4. Navigate the project folder using the cd command

> Navigate to the heart_disease_prediction folder

cd heart_disease_prediction

5. Install three libraries

PHP Gurukul

Welcome to PHPGurukul. We are a web development team striving our best to provide you with an unusual experience with PHP. Some technologies never fade, and PHP is one of them. From the time it has been introduced, the demand for PHP Projects and PHP developers is growing since 1994. We are here to make your PHP journey more exciting and useful.

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