Analytics and insights.
Explore the dataset and model information behind the HeartHealth assessment experience.
Feature overview
Distribution analysis of key features and their relationship with heart disease presence.
Feature correlation
Correlation matrix showing relationships between the features in the dataset.
Key insights
Context already present in the existing analysis.
Dataset characteristics
- 54.13% positive cases in the dataset
- No missing values reported
- Diverse age range with broad representation
- Mix of categorical and numerical features
Heart disease patterns
- Higher prevalence in males vs females
- Age has a relationship with disease risk
- Chest pain type is a strong predictor
- Exercise-induced angina shows clear patterns
Random Forest model
Current model details and performance metrics.
The assessment uses the existing trained model and StandardScaler with 13 clinical features from the UCI Heart Disease dataset.
Performance metrics describe the reference evaluation and do not guarantee an individual result. This system is for educational and research purposes only.
Dataset information
Model output should not replace professional medical advice, diagnosis, or treatment. Always consult a qualified healthcare professional for medical decisions.