Optimizing VO2max Prediction in Gamified Cardiac Assessment: Leveraging Effective Feature Selection and Refined Protocols for Robust Models

TL;DR

This study improves VO2max prediction accuracy in a gamified cardiac assessment by refining protocols and feature selection, demonstrating robust results across multiple machine learning models in a diverse cohort.

Contribution

It introduces an optimized CPSJT protocol with targeted feature extraction and evaluates multiple models, enhancing accessibility and prediction precision for large-scale screening.

Findings

Random Forest achieved RMSE of 5.15

All models showed strong correlations and low RMSE

Refined features improved model accuracy

Abstract

VO2max is a critical indicator of cardiopulmonary fitness, reflecting the maximum amount of oxygen the body can utilize during intense exercise. Accurately measuring VO2max is essential for assessing cardiovascular health and predicting outcomes in clinical settings. However, current methods for VO2max estimation, such as Cardiopulmonary Exercise Testing (CPET), require expensive equipment and the supervision of trained personnel, limiting accessibility for large-scale screening. Preliminary efforts have been made to create a more accessible method, such as the Cardiopulmonary Spot Jog Test (CPSJT). Unfortunately, these early attempts yielded high error margins, rendering them unsuitable for widespread use. In our study, we address these shortcomings by refining the CPSJT protocol to improve prediction accuracy. A crucial contribution is improved feature extraction which include gender, body mass index, aerobic duration, and anaerobic duration. This targeted approach helps in streamlining the model to enhance prediction precision while minimizing the risk of overfitting. In a cohort of 44 participants from the Indian population, we assessed the performance of various machine learning models using these features. With Stratified 5-Fold Cross-Validation, the Root Mean Squared Error (RMSE) values were 5.78 for Linear Regression, 5.15 for Random Forest, and 5.17 for Support Vector Regression. All models demonstrated strong test correlations and low RMSE values, underscoring their robust and reliable performance.