Impact and Classification of Body Stature and Physiological Variability in the Acquisition of Vital Signs Using Continuous Wave Radar

This work addresses this gap by applying machine learning (ML) algorithms—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest—to classify 92 subjects based on gender, age, Body Mass Index (BMI), and Chest Wall Perimeter (CWP). The results showed that the Random Forest algorithm was the most accurate, achieving accuracies of 76.66% for gender, 71.13% for age, 72.52% for BMI, and 74.61% for CWP.