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Filtrado por: Creador Halliday, Nnennaya Eliminar la restricciónCreador: Halliday, Nnennaya Departamento - School of Information Technology Eliminar la restricciónDepartamento: - School of Information Technology

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  • PREDICTION AND DETECTION OF DEPRESSION USING FOREST TREE ALGORITHM

    Article thumbnail: PREDICTION AND DETECTION OF DEPRESSION USING FOREST TREE ALGORITHM
    Type:
    Article
    Descripción/Resumen:
    Depression is a common illness that negatively affects feelings, thoughts and behaviors and can harm regular activities like sleeping. It is a leading cause of disability and many other diseases (Choudhury, et al 2013, Mathur et al, 2016, Watkins et al, 2013). According to WHO (World Health Organization) 1 statistics, more than 300 million people over the world are affected in depression and in each country at least 10% are provided treatment. Poor recognition and treatment of depression may aggravate heart failure symptoms, precipitate functional decline, disrupt social and occupational functioning, and lead to an increased risk of mortality (Cully, et al 2009). Early detection of depression is thus necessary. Unfortunately the rates of detecting and treating depression among those with medical illness are quite low (Egede, 2007). This research proposes a solution of using random forest classifier algorithm to detect and predict detection. A mobile application will be developed in order to collect user data and make prediction.
    Creador/Autor:
    Halliday, Nnennaya
    Peticionario:
    Jess Kropczynski
    Fecha modificada:
    05/15/2020
    Fecha modificada:
    05/15/2020
    Fecha de creacion:
    2019-04-11
    Licencia:
    All rights reserved