SICEF: Multiplatform Mobile Application based on Machine Learning to improve the Physical Assessment Process in a Barracks

The purpose of this applied research with a quantitative approach and experimental design in the pre-experimental modality was to improve the physical evaluation process through the implementation of a multiplatform mobile application based on machine learning to improve the physical evaluation process of the 32nd Brigade Barracks in Trujillo - Peru, for which record sheets were used, in order to measure the average time of clarification of questions of notes in evaluators, physical evaluation, obtaining results and to know the optimal military personnel, before and after the implementation of an application. mobile, which was directed based on the Mobile-D methodology, following the phases: Exploration, Initialization, Production, Stabilization and Testing, which was executed in a period of 5 months, from August to December 2021, corresponding to the measurements before the months of June to July 2021 and measurements after the months of November to December 2021. The main results of the indicators after the implementation of the application were that the average time of clarification of question marks in evaluators decreased from 3h 55m 8s to 12m 13s, the average time per physical evaluation decreased from 1h 9m 30s at 1h 9m 13s, the average time to obtain results decreased from 5d 12h 8m 34s to 3h 49m 54s and the average time to meet the optimal military personnel decreased from 4d 10h 48m 0s to 1d 5h 25m 11s. With the support of the parametric T-Student test, it is concluded that this implementation has a significant influence on the average time indicators for clarifying questions of notes in evaluators, obtaining results and knowing the optimal military personnel and is indistinct for the time indicator average by physical evaluation.