Examinando por Autor "Arevalo Rodriguez, William Fabian"
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- ÍtemModelos de machin elearning para la predicción del estado de salud prenatal y la prevención mediante cardiotocogramas.(Fundación Universitaria Los Libertadores. Sede Bogotá., ) Arevalo Rodriguez, William Fabian; Jiménez Prieto, Ingrid Natalia; Gonzales Veloza, Jose Jhon FreddyReducing child mortality is reflected in several of the UN Goals and is a key indicator of human progress. progress. CTGs are a simple and affordable option for assessing fetal health, allowing health professionals to take action to prevent infant and maternal mortality. health professionals to take action to prevent infant and maternal mortality. The main objective of this The main objective of this work is the implementation and evaluation of various machine learning models in order to determine the best in terms of construction, computational cost and accuracy in the classification (diagnosis) of the state of the fetus. Because of this, we propose a tournament of machine learning models that allow to find a balance between an easy to replicate and apply model and an easy to use model. easy to replicate and apply and a high sensitivity and accuracy in terms of fetal status prediction. Therefore, a list of different techniques a list of different supervised classification techniques are trained on a dataset provided by a plan that drives the automation of the automatic analysis of automation of automatic CTG analysis. Those models that performed best in accuracy require gradient boosting techniques. require gradient augmentation techniques where a high accuracy value is achieved, such models reveal that the accelerations and variability of the that accelerations and abnormal variability on short and long timescales play an important role in determining health status. health status. Among the models tested, the GBS presents the best results reaching an accuracy of 96.0% for the categorization of health status. 96.0% for fetal status categorization.