Heart disease prediction using machine learning is one of the most meaningful use cases in healthcare analytics.
This project evaluates patient medical records such as age cholesterol blood pressure and chest pain type to estimate the risk of heart disease.
Using Python and machine learning, the model classifies a patient into either heart disease present or heart disease not present.
This helps in early detection and supports better preventive medical decisions.
Project Overview
- Machine learning based risk prediction system
- Uses logistic regression for classification
- Built using Python and associated scientific libraries
Libraries Used
- Pandas for data manipulation
- NumPy for numerical computation
- Matplotlib for visualizing insights
- Scikit Learn for model building and evaluation
- StandardScaler for feature scaling and normalization
Dataset Details
- Dataset includes clinical attributes related to cardiovascular health
- Features include age sex cholesterol level chest pain type resting BP and max heart rate
- Target column indicates presence of disease as one or absence as zero
Preprocessing Steps
- Checked for missing values and corrected inconsistencies
- Split dataset into input features and target label
- Standardized features for improved learning performance
Model Building
- Logistic Regression selected as classification algorithm
- Training and testing split applied for evaluation
- Model learns mapping between medical attributes and disease outcome
Performance and Accuracy
- Accuracy evaluated for training and test data
- Results average between eighty three to eighty six percent
Prediction Flow
- User supplies patient values as input
- Converted to NumPy array and reshaped
- Output one means disease detected
- Output zero means disease not present
Deployment Possibilities
- Can be deployed using Streamlit or Flask web app
- Users enter values and get instant prediction results
Key Takeaways
- Complete end to end ML pipeline executed successfully
- Demonstrates health prediction using real patient data
- Shows potential for decision support in medical analysis
Future Enhancements
- Test advanced ML models for performance improvement
- Use hyperparameter tuning and cross validation
- Add interface for clinical usability and online access