ABSTRACT
Anemia is the most common blood disorder in the world, affecting millions of people yearly. It has multiple causes and jeopardizes development, growth and learning. Current tools provide non-hematologist doctors with just numbers. The present paper proposes a web application intended to provide doctors at Celia Sánchez Manduley Hospital in Manzanillo with a tool for determining the morphologic type of anemia, creating a list of possible causes, and also storing patient data in a database for future researches. As an information system, the website constitutes a powerful tool for the decision-making process, in particular, the diagnostic process, intended to provide more detailed information on anemia as well as to foster the information management at the hospital. For the application, HTML, CSS, JavaScript and PHP were used as languages; Apache as a web server; CodeIgniter as a PHP framework; MariaDB as a database management system; and Visual Studio Code as a development environment(AU)
La anemia afecta a millones de personas anualmente, esta obedece a múltiples causas y compromete el crecimiento, desarrollo y aprendizaje. Las herramientas actuales ofrecen al médico no especialista en hematología solo cifras. El presente trabajo propone una aplicación web con la cual se pretende brindar a los médicos del Hospital Celia Sánchez Manduley en Manzanillo una herramienta para la determinación del tipo morfológico de anemia, la elaboración de un listado de las posibles causas que la originan, así como el almacenamiento de datos de los pacientes para futuras investigaciones, y con ello contribuir a la toma de decisiones, en particular, al proceso de diagnóstico, y a una mejor gestión de información en el hospital. Para la aplicación se utilizaron los lenguajes HTML, CSS, JavaScript y PHP, el servidor web Apache, el framework PHP CodeIgniter, el gestor de base de datos MariaDB y el entorno de desarrollo Visual Studio Code(AU)
Subject(s)
Humans , Male , Female , Computer Communication Networks , Medical Informatics Applications , Programming Languages , Information Storage and Retrieval/methods , Anemia/diagnosis , Anemia/epidemiologyABSTRACT
The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts.
