Developing cardiometabolic risk classifiers for youth using handgrip strength, anthropometrics, and demographics: a machine learning approach leveraging National Health and Nutrition Examination Survey Data

Abstract

Background Handgrip strength associates with cardiometabolic (CMB) risk. Health-related physical fitness (HRPF) testing in schools do not explicitly predict CMB disease risk, and commonly engender anxiety, teasing and taunting by peers. Further, school-based screening for hyperinsulinemia using acanthosis nigricans likely misses nascent CMB risk factors like dyslipidemia. This study examined the feasibility of leveraging anthropometrics, demographics, and handgrip strength data to build optimal CMB risk classifiers. Methods The 2011-2014 National Health and Nutrition Examination Survey data from participants aged 12-18 years (n = 402; 205 males) (15.4 ± 1.8 years; 167.4 ± 9.1 cm; 73.2 ± 21.5 kg) who performed bilateral handgrip strength and CMB testing was leveraged. CMB risk was delineated as clustering of three or more risk factors across weight status, mean systolic, mean diastolic, HDL-cholesterol, LDL-cholesterol, total cholesterol, triglycerides, fasting glucose and HOMA-IR. 80% of the balanced dataset was used to train several models (e.g., Decision Tree and K-Nearest Neighbors (KNN)), while 20% was retained for further validation. There were 18 initial features, including age, sex, race, BMI, and combined handgrip strength. SelectKBest, Recurrent Feature Elimination, and Random Forest were deployed to identify the most salient features. Results Resulting models were evaluated using performance metrics such as Area Under the Curve (AUC), recall and precision. The most salient model was a Quadratic Discriminant model involving five features, namely number of people in household, annual household income, number of children 5 years or younger, combined handgrip strength, and waist circumference. When deployed, the model accurately classified 83% and 93% of the positive and negative classes within the test data, respectively (accuracy = 81.7%; AUC = 0.87; Recall = 0.91; Precision = 0.81; F-Measure = 0.92). Conclusions Findings demonstrate demographics, anthropometrics, and handgrip strength can be leveraged (using machine learning techniques) to accurately predict and optimally identify nascent CMB risk in youth while mitigating peer shaming and optimizing student participation in HRPF surveillance protocols in schools. Additional studies are needed to externally validate resulting models and investigate related effects on participation in HRPF testing and CMB risk detection among children and youth.

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

“…Toyin Ajisafe utilized six methods of machine learning algorithm, which are coarse tree, quadratic discriminant, logistic regression, kernel naïve Bayes, quadratic SVM, and weighted KNN, to see if it was possible to use anthropometrics, demographics, and handgrip strength data to build optimal Cardiometabolic (CMB) risk classifiers [18]. The researcher found that the coarse tree outperformed the other machine learning method with an accuracy of 85% in classified CMB risk data.…”