Predictive analysis of dominant hand grip strength among young children aged 6–15 years using machine learning techniques: a decision tree and regression analysis
Bibliographic record
Abstract
Background This study aimed to investigate and understand predictor variables and isolate the exact roles of anthropometric and demographic variables in the hand grip strength of young children. Material and methods In total, 315 male and female children participated in the study and 11 participants were excluded, therefore, 304 participants completed the assessments. Anthropometric measurements were collected at the time of study, along with age, height, weight, circumference of the hand, hand span, hand length, palm length, and hand grip strength (HGS) was measured. Both decision tree and regression machine learning analyses were used to isolate the relative contribution of independent features in predicting the targeted grip strength of children. Results Two predictive models were developed to understand the role of predictor variables in dominant hand HGS for both boys and girls. For boys, the decision tree was found to be the best model with the lowest error in predicting HGS. The respondents’ age, hand span, and weight were the most significant contributors to male hand grip strength. For the boys under 9.5 years of age, based on the decision tree analysis, weight (split at 27.5 kg) was found to be the most significant predictor. Furthermore, for the boys under 14.5 years of age, weight (split at 46.7 kg) remained the most important predictor. For boys 14.5 years and older, hand span was important in predicting handgrip strength. Backward regression was found to be the best model for predicting female hand grip strength. The R2 value for the model was 0.6646 and the significant variables were body mass index (BMI), hand length, hand span, and palm length, showing significance at a p-value of ≤0.05. This model predicted 66.46% of the variance in handgrip strength among the girls. Conclusion Anthropometric factors played a significant role in hand grip strength. Age, weight, and a larger hand span were found to be significant in impacting male HGS, while BMI, hand length, and palm length contributed to higher grip strength among the girls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".