Body Performance Analysis with Machine Learning and ANOVA Methods
Bibliographic record
Abstract
Detection of body performance with artificial intelligence using body data can provide a precise and objective evaluation of individuals' physical abilities. Artificial intelligence models can create personalized training programs and development plans by learning from large data sets. This can help athletes and fitness enthusiasts optimize their performance and reduce their risk of injury. Additionally, health professionals and coaches can make more effective and targeted interventions by making data-based decisions. In this study, it was aimed to predict the performances of athletes using body data. A dataset containing 13,393 rows of data in total was used. There are four classes in the dataset and they represent performance levels. Artificial Neural Network (ANN), Gradient Boosting (GB), Random Forest (RF) machine learning methods were used to classify the data. The cross validation method was used to objectively evaluate the results of the models. Confusion matrix and performance metrics were used to analyze the performance of the models. Classification successes and other performance metrics obtained as a result of the classification of the models were used to compare the performances of the models. The highest classification success of 74.5% was obtained from the ANN model. The lowest classification success was obtained from the RF model with 69.6%. ANOVA was used to examine the effects of the features in the dataset used on classification. The effects of the features were analyzed and their importance level was determined. It is thought that the proposed models can be used in applications by using them in performance analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".