Machine learning-powered anomaly detection system for monitoring athlete performance and biological passport data to identify potential doping violations, applies Random Forest, XGBoost, and support vector machine models to analyze inter-athlete and intra athlete performance metrics across sports
	B R R; GUPTA J; MALAVE S; CHAVAN S; ANUSHA V; AZIZ A; DALVI A; CHOTHANI A; JAIN M; JAGWANI P M
	2025-03-28
专利权人	JAGWANI P M (JAGW-Individual)
申请日期	2025-03-28
专利号	IN202521029921-A
成果简介	NOVELTY - The system applies Random Forest, XGBoost, and support vector machine (SVM) models to analyze inter-athlete and intra athlete performance metrics across various sports for enabling precise detection of unnatural performance improvements. The system ensures real-time data analysis and predictive insights while allowing anti-doping authorities to efficiently monitor athletes and prioritize the athletes with the highest risk of irregularities. The Random Forest based models are used for Hematological and Steroidal modules. The system continuously monitors an athlete's biological markers such as hemoglobin, hematocrit, testosterone, and epitestosterone, and compares longitudinal data trends to detect suspicious fluctuations indicative of performance-enhancing substance use. USE - Machine learning-powered anomaly detection system for monitoring athlete performance and biological passport (ABP) data to identify potential doping violations in sports organizations and anti-doping agencies. ADVANTAGE - The system combines structured data storage with artificial intelligence (AI)-driven anomaly detection, and offers an automated, scalable, and data-driven approach to athlete monitoring, thus improving the accuracy and efficiency of review processes. The system has a structured design that ensures efficient storage, retrieval, and analysis of diverse datasets, including athlete performance metrics and biological passport data. The system enhances detection accuracy by adapting to evolving doping methods while reducing false positives.
IPC 分类号	G06F-021/55 ; G06N-020/00 ; G06N-007/01 ; G06Q-010/0639 ; G16H-050/80
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/13464
专题	中国科学院新疆生态与地理研究所
作者单位	JAGWANI P M (JAGW-Individual)
推荐引用方式 GB/T 7714	B R R,GUPTA J,MALAVE S,et al. Machine learning-powered anomaly detection system for monitoring athlete performance and biological passport data to identify potential doping violations, applies Random Forest, XGBoost, and support vector machine models to analyze inter-athlete and intra athlete performance metrics across sports. IN202521029921-A[P]. 2025.

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