AI-based approach for improving the detection of blood doping in sports
This addresses the challenge of unfair practices in sports for officials, though it appears incremental as it builds on existing indirect testing methods.
The paper tackled the problem of detecting blood doping in sports by proposing an AI-based approach using blood parameters to identify rhEPO presence, aiming to improve decision-making over costly laboratory methods.
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.