Comparing Very-low Density Lipoprotein Cholesterol rates to High-sensitivity C-reactive protein

In this project, I developed a hybrid Convolutional Neural Network (CNN) to analyze the relationship between VLDL-P (Very Low-Density Lipoprotein Particle) and hsCRP (high-sensitivity C-Reactive Protein) rates, both biomarkers of cardiovascular health. I built a local Dash app to visualize cardiovascular risk levels based on these rates and provided insights into individual risk profiles. Additionally, I created a predictive model using a Random Forest Regressor to forecast hsCRP levels based on VLDL-P rates, uncovering a significant positive correlation between the two variables. This project demonstrates the potential of machine learning in identifying hidden patterns in cardiovascular biomarkers to improve risk assessment.