Heart Disease Prediction using Python & Machine Learning – Final Year Project

Heart-Disease-Prediction-python-ml
5.62$ 33.78$
  • INR: ₹ 499.00
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A complete Final Year Project on  Heart Disease Prediction using Python & Machine Learning for BCA/MCA/B.Tech/M.Tech students. Includes source code, database, Final Year project report, diagrams (ER, DFD, Use Case), and PowerPoint presentation.

Heart Disease Prediction ML Project Package Includes:

  • Full Source Code of the project.
  • Project Report (in doc and pdf formats, 41pages).
  • Project PPT

The project and Report are Downloadable immediately after payment is successful.

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Heart-Disease-Prediction-python-ml
Heart Disease Prediction using Python & Machine Learning – Final Year Project
5.62$ 33.78$
  • INR: ₹ 499.00

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🚀 Project Title: Heart Disease Prediction using Machine Learning
🧠 Tech Stack: Python, Scikit-learn, Pandas, Numpy,SQLITE 
🎓 Ideal For: BCA, MCA, B.Tech, M.Tech Final Year Students
📁 Includes: Source Code + Project Report + PPT + Installation Guide

The Heart Disease Prediction System is designed as a web-based platform powered by Django and a Machine Learning model. The user interacts with a simple web form to enter their medical details. Once submitted, the system validates the input, processes it using a trained Random Forest Classifier, and returns a prediction along with the probability of heart disease.

The system uses three-level risk classification:

  1. Low Risk: Probability < 50% and no critical parameters triggered.
  2. Borderline Risk: Probability between 50% and 69%.
  3. High Risk: Probability ≥ 70% or critical health indicators present.

Flow of the Proposed System:

  1. User Input:
    • The user fills in the form with required medical parameters: age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, ECG results, max heart rate, exercise-induced angina, ST depression (oldpeak), slope, number of major vessels, and thalassemia type.
  2. Validation:
    • The system ensures that the input values are within valid ranges.
  3. Prediction:
    • The input is reshaped into the correct format and passed to the ML model.
  4. Risk Classification:
    • Based on probability and override rules, the system assigns a risk category.
  5. Result Display:
    • The system displays the prediction result, probability score, and relevant interpretation notes.
  6. Database Storage:
    • The system stores the prediction, probability, and input details in the database.
  7. History and Filtering:
    • The user can access previous predictions and filter by risk type.

This approach ensures that even non-technical users can easily assess their heart disease risk using a structured and reliable process.

 

🔧 Software Requirements:

  • Python 3.x

  • Scikit-learn, Pandas, NumPy,

  • SQLite

  • Django(for web interface)

  • Any Code Editor (VS Code, PyCharm, etc.)

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