Mental Depression Deduction Using Modified Regression Model to Prevent Suicidal Attempt

This study explores a novel approach for predicting depression using association-based multilevel linear regression. The suggested approach, known as association-based multilevel linear regression, uses data on mental depression to predict the prevalence of depression. Several statistical techniques can be used to forecast depression. Several statistical methods, including Linear Regression (LR), Multilevel Linear Regression (MLR), Naïve Bayes algorithm and Decision Tree (DT) were used in this investigation. Because these algorithms are able to predict mental depression based on certain characteristics such as precision and efficiency, their performance reduces. The results of these algorithms' predictions vary significantly, especially in terms of accuracy. The mental health data is fed into a developed model that has been trained to make predictions in order to address the aforementioned problem. Depression is the subject of conversation. A great degree of accuracy is shown by the association-based multilevel linear regression technique and the evaluation of prediction of accuracy in relation to other statistical methods. This study used association-based multilevel linear regression technique.  When compared to traditional methods, the method exhibits a substantially greater level of accuracy, almost 99%

Identifying Depression in the National Health and Nutrition Examination Survey Data using a Deep Learning Algorithm

Background: As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression. Methods: Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4,949 from the South Korea NHANES (K-NHANES) database in 2014. Results: A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) of 0.91 and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The deep-learning algorithm trained with serial datasets (NHANES, from 1999 to 2012), predicted the prevalence of depression in the following two years of data (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained with NHANES could further predict depression in K-NHANES. There, logistic regression had the highest performance (AUC, 0.77) followed by deep learning algorithm (AUC, 0.74). Conclusions: Deep neural-networks managed to identify depression well from other health and demographic factors in both the NHANES and K-NHANES datasets. The deep-learning algorithm was also able to predict depression relatively well on new data set—cross temporally and cross nationally. Further research can delineate the clinical implications of machine learning and deep learning in detecting disease prevalence and progress as well as other risk factors for depression and other mental illnesses

Dauruxu : detección de emociones de personas y sus actividades para el apoyo en la evaluación de factores de riesgo psicosocial

La evaluación de riesgos psicosociales ha desempeñado un papel dominante para garantizar el bienestar y la salud de las personas. No obstante, mecanismos como entrevistas y cuestionarios son susceptibles de obtener resultados sesgados debido a la falta de datos que no se pueden adquirir durante las evaluaciones. Este trabajo propone una arquitectura para identificar actividades y emociones implícitas en los cuestionarios actuales y que tienen el potencial de ser detectadas por cámaras. Mediante visión por computadora, se extraen características de los fotogramas de video los cuales son empleados como predictores para tareas de clasificación. La cuantificación de indicadores basada en la detección de actividades y emociones brindará datos adicionales para respaldar las evaluaciones de riesgo psicosocial.Psychosocial risk assessment has played a dominant role in ensuring the well-being and health of people. However, mechanisms such as interviews and questionnaires are susceptible to obtaining biased results due to the lack of data that cannot be acquired during evaluations. This work proposes an architecture to identify activities and emotions implicit in current questionnaires and that have the potential to be detected by cameras. Through computer vision, features are extracted from the video frames which are used as predictors for classification tasks. The quantification of indicators based on the detection of activities and emotions will provide additional data to support psychosocial risk assessments.Magíster en Ingeniería de Sistemas y ComputaciónMagíster en Analítica para la Inteligencia de NegociosMaestrí