Gene expression analysis of 1000 genes across 100 samples using machine learning techniques.
The left panel shows a Principal Component Analysis (PCA) plot, where each point represents a sample.
Blue areas indicate samples predicted as healthy, while red areas indicate samples predicted as diseased by the AI model.
Key samples are labeled. The right panel displays a heatmap of the expression levels for the top 10 contributing genes across all samples,
with yellow indicating high expression and purple indicating low expression.
This visualization helps identify patterns in gene expression that distinguish between healthy and diseased states.
Demo: AI Insights on Health Risks Through Biomarkers
This plot illustrates how AI analyzes health indicators to predict patient risk,
using a synthetic dataset of 40 patients (20 healthy, 20 at-risk).
Green and red dots represent healthy and at-risk patients respectively, based on two key health metrics.
The background color gradient shows the AI's decision boundaries, with green areas deemed healthy and red areas
considered at-risk. By learning patterns from this data, the AI model can quickly assess new patients,
potentially enabling early intervention and more efficient health screenings. While the model shows clear
separation between groups, some outliers highlight the importance of combining AI insights with professional medical
judgment. This simplified visualization demonstrates the potential of AI in healthcare risk assessment using just two indicators,
though real-world applications would likely involve many more health factors.