Attribute Reduction Algorithm Based Early Warning Model of Sports Injury
Abstract
With the continuous development of track and field sports, a relatively complete and scientific system has been formed in terms of training, and more attention has been paid to the planned training of teenagers for many years, psychological training and recovery training, and strengthening medical supervision and scientific research. In order to speed up the scientific training of track and field and improve the level of track and field training in China, in view of the characteristics of track and field injuries such as multiple causes and complexity, this paper analyzes and studies the attribute reduction algorithm based on attribute reduction algorithm, and proposes a sports injury early warning model based on mutual information. Taking the reduction results as input neurons, a BP neural network with hidden layer is established in MATLAB environment. The results show that: call 85% of the track and field athletes in the sample information as training samples for training. After the simulation experiment of the model, it is found that the error value of the prediction results is basically controlled in the range of-0.025-0.05, which meets the requirements of early warning accuracy of injury risk level of athletes, and the accuracy rate of early warning of sports injury risk reaches 100%.
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Leihua, Z. , Hongtai, N. , Zhongni, W. , & Xuehong, L. . (2019). Research on the construction of early warning model of criminals based on big data. Netinfo Security. https://doi.org/10.1007/978-981-32-9563-6_27
Zhou, D. . (2019). Research on the security early warning model of campus network based on log. IOP Conference Series Earth and Environmental Science, 252, 042080. doi:10.1088/1755-1315/252/4/042080
Juan, D. , Yue, W. , Wei, J. , & Shuyuan, L. . (2019). Research on the Early-warning Model of Foaming Phenomenon in Desulfurization Solution System of Natural Gas Purification Plant Based on Artificial Intelligence Technology. 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD). https://doi.org/10.1016/j.desal
Jin, R. , & Rui, C. . (2019). Research on early warning model of microblog public opinion based on netizens' interest preferences. China Public Security(Academy Edition). doi:10.1088/1742-6596/1631/1/012108
Yang, P. , & Yang, M. . (2019). Research on the Management Model of University Students Academic Early Warning Based on Big Data Analysis. International Conference on Communications, Information System and Computer Engineering. North China University of Technology;Beijing University of Chemical Technology.DOI: 10.1109/CISCE.2019.00148
Yu, C. , Li, Z. , & Liu, H. . (2019). Research on Gravity Compensation Algorithm of Model-free Manipulator Based on L1-norm Convex Optimization. 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE. DOI: 10.1109/ROBIO.2018.8664862
Wang, X., (2020). Research on inversion of ecosystem dynamics model parameters based on improved neural network algorithm. Microprocessors and Microsystems, 80. DOI: 10.1016/j.micpro.2020.103605
Jiang, H., Xue, Z. P. , & Guo, Y. . (2020). Research on plant leaf disease identification based on transfer learning algorithm. Journal of Physics Conference Series, 1576, 012023. https://doi.org/10.1155/2020/2479172
Qi, Z. , Li, S. , Li, M. , Dang, C. , Sun, D. , & Zhang, D. , et al. (2019). Research on the algorithm model for measuring ocean waves based on satellite gps signals in china. Sensors (Basel, Switzerland), 19(3). https://doi.org/10.3390/s19030541
Xin, C. , Dan, L. , & Shuo, H. . (2020). Research on Deceleration Early Warning Model Based on V2X. 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). https://doi.org/10.1155/2020/5172305
Jia, X., & Zhang, D. (2021). Prediction of maritime logistics service risks applying soft set based association rule: An early warning model. Reliability Engineering & System Safety, 207, 107339. DOI: 10.1016/j.ress.2020.107339
Lowe, D., Ebi, K. L., & Forsberg, B. (2011). Heatwave early warning systems and adaptation advice to reduce human health consequences of heatwaves. International journal of environmental research and public health, 8(12), 4623-4648. doi: 10.3390/ijerph8124623
Delibaic, B., & Obradovic, Z. (2012, April). Towards a DGSS prototype for early warning for ski injuries. In 2012 IEEE 28th International Conference on Data Engineering Workshops (pp. 68-72). IEEE. DOI: 10.1109/ICDEW.2012.73
Ahmed, B., Rahman, M. S., Sammonds, P., Islam, R., & Uddin, K. (2020). Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh. Geomatics, Natural Hazards and Risk, 11(1), 446-468. doi/full/10.1080/19475705.2020.1730988
Zhang, K., Zheng, X., Li, H., & Zhao, Z. (2020). Human health risk assessment and early warning of heavy metal pollution in soil of a coal chemical Plant in Northwest China. Soil and Sediment Contamination: An International Journal, 29(5),
-502. DOI: 10.1080/15320383.2020.1746737
Fu, Z., Zhang, H., Chen, Q., & Liu, S. (2016, December). Application of naive bayes classifier in stampede risk early-warning of large-scale activities. In 2016 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) (pp. 174-180). IEEE. DOI:10.1371/journal.pone.0150630
Fu, Z., Zhang, H., Chen, Q., & Liu, S. (2016, December). Application of naive bayes classifier in stampede risk early-warning of large-scale activities. In 2016 International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) (pp. 174-180). IEEE. doi: 10.21037/atm.2016.03.38
Becker, J. S., Potter, S. H., Prasanna, R., Tan, M. L., Payne, B. A., Holden, C., ... & Johnston, D. M. (2020). Scoping the potential for Earthquake Early Warning in Aotearoa New Zealand: a sectoral analysis of perceived benefits and challenges. International Journal of Disaster Risk Reduction, 51, 101765. https://doi.org/10.5194/nhess-2021-42
Xu, J., & Liu, Y. (2019). CET-4 score analysis based on data mining technology. Cluster Computing, 22(2), 3583-3593. https://doi.org/10.1007/s10586-018-2208-x
Laxminarayan, S., Rakesh, V., Oyama, T., Kazman, J. B., Yanovich, R., Ketko, I., ... & Reifman, J. (2018). Individualized estimation of human core body temperature using noninvasive measurements. Journal of Applied Physiology, 124(6), 1387-1402. doi: 10.1152/japplphysiol
López-Valenciano, A., Ayala, F., Puerta, J. M., Croix, M. D. S., Vera-García, F., Hernández-Sánchez, S., & Myer, G. (2018). A preventive model for muscle injuries: a novel approach based on learning algorithms. Medicine and science in sports and exercise, 50(5), 915. doi: 10.1249/MSS.0000000000001535
Fujinawa, Y., & Noda, Y. (2013). Japan's earthquake early warning system on 11 March 2011: performance, shortcomings, and changes. Earthquake Spectra, 29(1_suppl), 341-368. https://doi.org/10.1193/1.4000127
Sparks, R., Carter, C., Graham, P., Muscatello, D., Churches, T., Kaldor, J., & Ryan, L. (2010). Understanding sources of variation in syndromic surveillance for early warning of natural or intentional disease outbreaks. IIE Transactions, 42(9), 613-631. DOI: 10.1080/07408170902942667
Laxminarayan, S., Rakesh, V., Oyama, T., Kazman, J. B., Yanovich, R., Ketko, I., & Reifman, J. (2018). Individualized estimation of human core body temperature using noninvasive measurements. Journal of Applied Physiology, 124(6), 1387-1402. doi: 10.1152/japplphysiol.
Hepworth, P. J., Nefedov, A. V., Muchnik, I. B., & Morgan, K. L. (2010). Early warning indicators for hock burn in broiler flocks. Avian Pathology, 39(5), 405-409. doi: 10.1136/vr.104066
Kumar, R., & Saini, H. (2020). Secure, Resilient and Green Computing in Wireless Sensor Networks. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 13(2), 128-129. https://doi.org/10.1007/s12083-016-0511-y
Sharma, A., & Kumar, R. (2017). A framework for pre-computated multi-constrained quickest QoS path algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(3-6), 73-77. https://doi.org/10.1016/j.future.2017.09.010
Leddy, J. J., Baker, J. G., Merchant, A., Picano, J., Gaile, D., Matuszak, J., & Willer, B. (2015). Brain or strain? Symptoms alone do not distinguish physiologic concussion from cervical/vestibular injury. Clinical journal of sport medicine, 25(3), 237-242. doi: 10.1097/JSM.0000000000000128
Walter, S. D., Hart, L. E., McIntosh, J. M., & Sutton, J. R. (1989). The Ontario cohort study of running-related injuries. Archives of internal medicine, 149(11), 2561-2564. https://doi.org/10.1001/archinte.
Quaid, M. A. K., & Jalal, A. (2020). Wearable sensors based human behavioral pattern recognition using statistical features and reweighted genetic algorithm. Multimedia Tools and Applications, 79(9), 6061-6083. https://doi.org/10.1007/s11042-019-08463-7.
Akutsu, T., Miyano, S., & Kuhara, S. (2000). Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. Journal of computational biology, 7(3-4), 331-343. doi: 10.1089/106652700750050817.
Goodlin, G. T., Roos, A. K., Roos, T. R., Hawkins, C., Beache, S., Baur, S., & Kim, S. K. (2015). Applying personal genetic data to injury risk assessment in athletes. PloS one, 10(4), e0122676.DOI:10.1371/journal.pone.0122676.
DOI: https://doi.org/10.31449/inf.v45i5.3560
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