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.
🚀 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:
- Low Risk: Probability < 50% and no critical parameters triggered.
- Borderline Risk: Probability between 50% and 69%.
- High Risk: Probability ≥ 70% or critical health indicators present.
Flow of the Proposed System:
- 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.
- Validation:
- The system ensures that the input values are within valid ranges.
- Prediction:
- The input is reshaped into the correct format and passed to the ML model.
- Risk Classification:
- Based on probability and override rules, the system assigns a risk category.
- Result Display:
- The system displays the prediction result, probability score, and relevant interpretation notes.
- Database Storage:
- The system stores the prediction, probability, and input details in the database.
- 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:
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Python 3.x
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Scikit-learn, Pandas, NumPy,
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SQLite
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Django(for web interface)
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Any Code Editor (VS Code, PyCharm, etc.)
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