Augmentation for Blood Doping Discovery in Sports using Random Forest Ensembles with LightGBM - Sasikala, D.; Sharma, K. Venkatesh (Journal of Ubiquitous Computing and Communication Technologies, 2022)
Athletics bureaucrats round the globe are tackling implausible encounters owing to the partial methods of customs executed by the athletes to progress their enactment in their sports. It embraces the intake of hormonal centred remedies or transfusion of blood to upsurge their power and the effect of their coaching. On the other hand, the up-to-date direct test of discovery of these circumstances embraces the laboratory-centred technique viz restricted for the reason that of the cost factors, handiness of medical experts, etc. This ends us to pursue for indirect assessments. By the emergent curiosity of Artificial Intelligence (AI) in healthcare, it is vital to put forward a process built on blood factors to advance decision making. In this research script, a statistical and machine learning (ML) centred tactic was suggested to ascertain the concern of doping constituent rhEPO in blood units.