02010nas a2200313   4500000000100000000000100001008004100002260001200043653002800055653003700083653001100120653002300131653001700154653001900171100003100190700002800221700002700249700002700276700002800303700002900331700002800360700002200388245010600410856008100516300000800597490001300605520106400618022001401682       9998                            d  c01/202310aArtificial Intelligence10aHuman-Computer Interaction (HCI)10aHealth10aInformation System10aMedical Data10aMedical Images1 aFrancisco García-Peñalvo1 aAndrea Vázquez-Ingelmo1 aAlicia García-Holgado1 aJesús Sampedro-Gómez1 aAntonio Sánchez-Puente1 aVíctor Vicente-Palacios1 aP. Ignacio Dorado-Díaz1 aPedro L. Sánchez00aKoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals  uhttps://www.ijimai.org/journal/sites/default/files/2023-01/ip2023_01_006.pdf  a1-80 vIn Press3 aMachine Learning (ML) has extended its use in several domains to support complex analyses of data. The medical field, in which significant quantities of data are continuously generated, is one of the domains that can benefit from the application of ML pipelines to solve specific problems such as diagnosis, classification, disease detection, segmentation, assessment of organ functions, etc. However, while health professionals are experts in their domain, they can lack programming and theoretical skills regarding ML applications. Therefore, it is necessary to train health professionals in using these paradigms to get the most out of the application of ML algorithms to their data. In this work, we present a platform to assist non-expert users in defining ML pipelines in the health domain. The system’s design focuses on providing an educational experience to understand how ML algorithms work and how to interpret their outcomes and on fostering a flexible architecture to allow the evolution of the available components, algorithms, and heuristics.  a1989-1660