Procesamiento de lecturas de actividad motriz para el desarrollo de un modelo de clasificación de pacientes con depresión y personas sanas.
Depression is a mental disorder that can become chronic and significantly hamper the performance of daily life. In its most serious form, it can lead to suicide. Motor activity measurements have become an emerging topic in the field of mental health. Several studies use sensors to measure movements...
| Autores: | , , |
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | México |
| Institución: | Universidad Autónoma de Zacatecas |
| Repositorio: | Repositorio Institucional Caxcán |
| Idioma: | español |
| OAI Identifier: | oai:http://ricaxcan.uaz.edu.mx:20.500.11845/2587 |
| Acceso en línea: | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/2587 |
| Access Level: | acceso abierto |
| Palabra clave: | INGENIERIA Y TECNOLOGIA [7] Depresión Diagnostico Actividad motriz Redes neuronales Profundas Aprendizaje automático Depresión dataset |
| Sumario: | Depression is a mental disorder that can become chronic and significantly hamper the performance of daily life. In its most serious form, it can lead to suicide. Motor activity measurements have become an emerging topic in the field of mental health. Several studies use sensors to measure movements of patients over time to create a diagnosis. The aim is to develop a model based on some machine learning techniques and genetic algorithms, to classify patients with depression and healthy people using motor activity. Readings of 55 patients (32 control patients and 23 patients with condition) were selected, during period of one week, obtaining a total of 385 observations (participants) and 1440 characteristics (time intervals) from which the intervals of one minute more representative to develop a machine learning model using algorithms such as: neural networks, logistic regression, random forests, vector support machines and deep neural networks where the latter obtained the best performance with 80.24% precision, it was trained with 270 of participants and was tested with remaining 30% of data, which 61 were correctly diagnosed as healthy and 32 with depression. Based on these results, it can be concluded that the implementation of these models in an assisted diagnostic tool can help avoid cases of severe depression. |
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