Physical Health Data Analysis of Youth Sports Based on Cloud Computing and Gait Perception

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Comparison of Performance Indexes of Random Forest Algorithm Before and After Improvement

Algorithm	Training time	Precision	Recall	F1 value	Accuracy
Random forest	128.26s	85.26%	85.47%	85.61%	86.74%
Improved random forest	110.14s	99.81%	99.56%	99.15%	99.92%

Data examples

Serial number	Height (cm)	Weight (kg)	50m dash time (s)	Mean grip strength (kg)
1	153.23	48.26	8.9	20.36
2	156.78	51.29	9.1	21.05
3	161.29	55.30	8.7	22.68
…	…	…	…	…

Comparison of Performance Indexes of Random Forest Algorithm Before and After Improvement of Decision Tree Feature Splitting Method

Decision tree feature splitting method	Training time	Precision	Recall	F1 value	Accuracy
CART	126.33s	84.33%	85.28%	84.31%	83.28%
ID3	128.26s	85.26%	85.47%	85.61%	86.74%
Multiple splitting method	58.36s	90.11%	90.26%	90.55%	90.27%

Comparison of Performance Indexes of Different Methods

Algorithm	Training time	Precision	Recall	F1 value	Accuracy
Logistic regression	155.37s	76.14%	76.22%	76.41%	75.08%
LightGBM	50.28s	91.23%	91.62%	92.27%	92.39%
Improved random forest	110.14s	99.81%	99.56%	99.15%	99.92%

Analysis Results of Different Algorithms on Physical Health Data of Different Categories of Youth Sports

Category	Accuracy	Logistic regression	LightGBM	Improved random forest
Competitive football	Top1	76.23%	90.12%	98.88%
	Top3	78.33%	92.63%	98.92%
	Top5	79.33%	93.18%	99.99%
Wrestling	Top1	76.17%	90.05%	98.81%
	Top3	78.29%	92.36%	99.23%
	Top5	79.65%	93.07%	99.91%
Sports acrobatics	Top1	74.32%	90.91%	98.23%
	Top3	76.48%	92.76%	99.24%
	Top5	78.91%	93.66%	99.78%

Comparison of Various Performance Indexes of Random Forest Algorithm Before and After Improvement of Sampling Mode

Sampling mode	Training time	Precision	Recall	F1 value	Accuracy
Bootstrap	128.26s	85.26%	85.47%	85.61%	86.74%
Improved random forests	142.39s	98.23%	98.14%	98.06%	98.41%