HeartRisk AI

About the HeartRisk AI Model

Model Overview
Understanding our AI-powered heart disease risk assessment

The HeartRisk AI model is a machine learning algorithm designed to predict an individual's risk of developing heart disease. It analyzes various health metrics and lifestyle factors to provide a personalized risk assessment.

Key Features

  • Utilizes advanced machine learning techniques
  • Trained on large-scale, diverse medical datasets
  • Considers multiple risk factors for comprehensive analysis
  • Provides personalized risk scores and recommendations
Training Data
The foundation of our AI model

Our model was trained on a diverse dataset comprising over 300,000 patient records from various sources, including:

  • Framingham Heart Study
  • NHANES (National Health and Nutrition Examination Survey)
  • UK Biobank
  • Electronic Health Records from multiple healthcare systems

This extensive dataset ensures that our model can account for a wide range of demographic and health profiles, enhancing its accuracy and applicability.

Model Performance: Confusion Matrix
Evaluation of our AI model's predictions

True Positive

85

False Positive

15

False Negative

10

True Negative

90

Accuracy: 87.5%

Precision: 85.0%

Recall: 89.5%

F1 Score: 87.2

This confusion matrix represents the performance of our AI model on a test dataset. It shows how well the model predicts both positive (at-risk) and negative (not at-risk) cases.

Model Limitations
Understanding the boundaries of our AI predictions

While our model strives for high accuracy, it's important to understand its limitations:

  • Predictions are based on population-level data and may not capture all individual variations
  • The model doesn't account for rare genetic factors or certain specific medical conditions
  • Accuracy may vary across different demographic groups
  • The assessment is not a substitute for professional medical advice or diagnosis

We recommend using the HeartRisk AI assessment as a starting point for discussions with healthcare providers and as a tool for monitoring general heart health trends.