AI-based approach for improving the detection of blood doping in sports

TL;DR

This paper proposes an AI-driven method using blood parameters and machine learning to enhance the detection of blood doping, specifically rhEPO, addressing limitations of traditional laboratory tests.

Contribution

It introduces a novel statistical and machine learning approach for indirect detection of blood doping, improving decision-making in sports anti-doping efforts.

Findings

Effective identification of rhEPO using blood parameters

Machine learning improves detection accuracy

Addresses cost and availability issues of direct tests

Abstract

Sports officials around the world are facing incredible challenges due to the unfair means of practices performed by the athletes to improve their performance in the game. It includes the intake of hormonal based drugs or transfusion of blood to increase their strength and the result of their training. However, the current direct test of detection of these cases includes the laboratory-based method, which is limited because of the cost factors, availability of medical experts, etc. This leads us to seek for indirect tests. With the growing interest of Artificial Intelligence in healthcare, it is important to propose an algorithm based on blood parameters to improve decision making. In this paper, we proposed a statistical and machine learning-based approach to identify the presence of doping substance rhEPO in blood samples.