Blood Biomarkers Predict Cardiac Workload Using Machine Learning.

INTRODUCTION: Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure product (rRPP) varies from one individual to the next, but its biochemical/cellular phenotype remains unknown. This study determined the degree to which an individual's biochemical/cellular profile as characterized by a standard blood panel is predictive of rRPP, as well the importance of each blood biomarker in this prediction. METHODS: We included data from 55,730 participants in this study with complete rRPP measurements and concurrently collected blood panel information from the Health Management Centre at the Affiliated Hospital of Hangzhou Normal University. We used the XGBoost machine learning algorithm to train a tree-based model and then assessed its accuracy on an independent portion of the dataset and then compared its performance against a standard linear regression technique. We further determined the predictive importance of each feature in the blood panel. RESULTS: We found a fair positive correlation (Pearson r) of 0.377 (95% CI: 0.375-0.378) between observed rRPP and rRPP predicted from blood biomarkers. By comparison, the performance for standard linear regression was 0.352 (95% CI: 0.351-0.354). The top three predictors in this model were glucose concentration, total protein concentration, and neutrophil count. Discussion/. CONCLUSION: Blood biomarkers predict resting RPP when modeled in combination with one another; such models are valuable for studying the complex interrelations between resting cardiac workload and one's biochemical/cellular phenotype.

Waist circumference prediction for epidemiological research using gradient boosted trees.

BACKGROUND: Waist circumference is becoming recognized as a useful predictor of health risks in clinical research. However, clinical datasets tend to lack this measurement and self-reported values tend to be inaccurate. Predicting waist circumference from standard physical features could be a viable method for generating this information when it is missing or mitigating the impact of inaccurate self-reports. This study determined the degree to which the XGBoost advanced machine learning algorithm could build models that predict waist circumference from height, weight, calculated Body Mass Index, age, race/ethnicity and sex, whether they perform better than current models based on linear regression, and the relative importance of each feature in this prediction. METHODS: We trained tree-based models (via XGBoost gradient boosting) and linear models (via regression) to predict waist circumference from height, weight, Body Mass Index, age, race/ethnicity and sex (n = 60,740 participants). We created 10 iterations of each model, each using 90% of the dataset for training and the remaining 10% for testing performance (this group was different for each iteration). We calculated model performance and feature importance as an average across 10 iterations. We then externally validated the ensembled version of the top model. RESULTS: The XGBoost model predicted waist circumference with a mean bias ± standard deviation of 0.0 ± 0.04 cm and a root mean squared error of 4.7 ± 0.05 cm, with performance varying slightly by sex and race/ethnicity. The XGBoost model showed varying degrees of improvement over linear regression models. The top 3 predictors were Body Mass Index, weight and race (Asian). External validation found that on average this model overestimated waist circumference by 4.65 cm in the United Kingdom population (mainly due to overprediction in females) and underestimated waist circumference by 1.7 cm in the Chinese population. The respective root mean squared errors were 7.7 cm and 7.1 cm. CONCLUSIONS: XGBoost-based models accurately predict waist circumference from standard physical features. Waist circumference prediction using this approach would be valuable for epidemiological research and beyond.