[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121974-en":3,"doc-seo-121974-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121974,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Predicting the Interference of Mental Health Illness at Work Productivity in the Technology Industry using Machine Learning Methods","Advancements in Machine Learning increasingly enable more accurate predictions across domains, including mental health and its effect on workplace productivity, particularly in the technology industry. This dissertation predicts work interference caused by mental health issues using Machine Learning techniques. Seven classification models were applied to a dataset from the non-governmental organization Open Sourcing Mental Illness, after data cleaning and preparation to preserve relevant variables. Models were evaluated with metrics such as Accuracy and Precision. Results show Gradient Boosting Classifier as the top performer with 83.2% accuracy.","Predicting the Interference of Mental Health Illness at Work Productivity in the Technology Industry using Machine Learning Methods  \nSamuel de Jesús Rodríguez Agudelo  \nDissertation written under the supervision of Professor Pedro Afonso  \nFernandes  \nDissertation submitted in partial fulfillment of requirements for th e MSc in Business Analytics, at the Universidade Católica Portuguesa, December 2023.  \nPredicting the Interference of Mental Health Illness at Work Productivity in the Technology Industry using Machine Learning Methods  \nSamuel de Jesús Rodríguez Agudelo  \nResumo  \nOs avanços nas tecnologias de aprendizagem automática estão a proporcionar progressivamente maiores benefícios, especialmente na realização de previsões precisas aplicáveis a diversos domínios. Um desses cenários de interesse crítico é a saúde mental e o seu impacto naprodutividade no local de trabalho, particularmente na indústria tecnológica. Esta tese tem como objetivo prever a interferência no trabalho decorrente de problemas de saúde mental no sector tecnológico utilizando técnicas de aprendizagem automática.  \nSete modelos de classificação de aprendizagem automática cuidadosamente seleccionados foram aplicados a um conjunto de dados provenientes da organização não governamental conhecida como Open Sourcing Mental Illness. A base de dados foi submetida a um processamento prévio, garantindo a retenção de todas as variáveis relevantes necessárias para satisfazer os requisitos de cada modelo e facilitar uma aplicação bem sucedida. Subsequentemente, os modelos foram rigorosamente avaliados utilizando várias métricas, incluindo Exatidão e Precisão, entre outras.  \nA investigação identificou o 'Gradient Boosting Classifier' como o modelo mais eficaz, apresentando um desempenho superior na maioria das medidas de previsão, incluindo uma precisão de 83,2% . Esta investigação também revelou limitações semelhantes às observadas em estudos anteriores de aprendizagem automática relacionados com a saúde mental, tal como referido na revisão da literatura. No entanto, os resultados contribuem com informações valiosas para a aplicação da aprendizagem automática na previsão da interferência no trabalho devido a doenças mentais, particularmente no panorama dinâmico da indústria tecnológica.  \nPalavras Chave: Aprendizado de Máquina, Modelos de Classificação, Gradient Boosting Classifier, Indústria Tecnológica, Saúde Mental.  \nPredicting the Interference of Mental Health Illness at Work Productivity in the Technology Industry using Machine Learning Methods  \nSamuel de Jesús Rodríguez Agudelo  \nAbstract  \nAdvancements in Machine Learning technologies are progressively providing enhanced benefits, especially in the domain of making accurate predictions applicable to diverse scenarios. One such scenario of critical interest is mental health and its impact on workplace productivity, particularly within the technological industry. This thesis aims to predict work interference arising from mental health issues in the technology sector using Machine Learning techniques. Seven carefully selected Machine Learning classification models were applied to a dataset sourced from the non-governmental organization known as Open Sourcing Mental Illness. The dataset underwent strategic cleaning and preparation, ensuring the retention of all relevant variables necessary to meet the requirements of each model and facilitate successful deployment. Subsequently, the models were rigorously evaluated using various measures of prediction, including Accuracy and Precision, among others.  \nThe research identified the 'Gradient Boosting Classifier' as the most effective model, exhibiting superior performance across the majority of prediction measures, including an 83.2% accuracy. This investigation also uncovered similar limitations to those observed in prior machine learning studies related to mental health, as discussed in the Literature Review. However, the findings contribute valuable insig","cbCairt3H6c8hp78","https://ap.wps.com/l/cbCairt3H6c8hp78","pdf",3472778,1,69,"English","en",105,"# INTRODUCTION\n# LITERATURE REVIEW\n## THE CONCEPT OF MENTAL HEALTH\n## HIDDEN COST OF MENTAL HEALTH ILLNESS FOR COMPANIES\n## CALCULATION OF THE ANNUAL COST OF LOST PRODUCTIVE TIME","[{\"question\":\"What does the dissertation aim to predict?\",\"answer\":\"It aims to predict work interference arising from mental health issues in the technology sector using Machine Learning techniques.\"},{\"question\":\"Which models and dataset were used in the study?\",\"answer\":\"Seven Machine Learning classification models were applied to a dataset sourced from the non-governmental organization Open Sourcing Mental Illness, after cleaning and preparation.\"},{\"question\":\"What model performed best and what was its accuracy?\",\"answer\":\"The Gradient Boosting Classifier achieved the strongest overall performance, with an accuracy of 83.2%.\"}]","Predicting the Interference of Mental Health Illness at Work Productivity in the Technology Industry using Machine Learning Methods | 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