A neural network-based method for automated assessment of well-being based on personnel screening questionnaires

Mark van Gils, Jukka Suovanen, Juho Merilahti, Juha Pärkkä, Henri Rautamo

    Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsScientific


    There is a need for objective, simple methods to assess employees' well-being. Currently, a healthcare professional assesses this partly using experience-based knowledge, making it difficult to use explicit rules to automate this task. We aimed to develop classifiers learning from data from questions in personnel screening questionnaires. Our goals were: a) to define an efficient subset of questions and b) to develop a classifier for well-being grading. Screening data from occupational health checks in a random selection of the working population in various parts of Finland were used. Data contained 98 answers on medication usage, pain experience, psychological factors, stress, lifestyle etc. A healthcare professional scored well-being on a scale from 1 to 3 (indicating seriousness in decrease of well-being). 1063 subjects were used. For a), relationships between variables were assessed using correspondence analyses. For b), linear and non-linear regression and neural networks (backpropagation networks) were used with as input the variables found from a) and as desired output the well-being scores. Final performance was assessed using an independent test set of 89 subjects. Significant correspondences between different questions were found. A subset of 9 independent questions proved to be efficient. These questions relate to weight, dizziness, sports activities, pain experience, views on life, and personal assessment of the ability to continue work. A neural network gave the best results with an accuracy of 83% on the test set, and sensitivity 85% and specificity 83% for separating 'reduced' from 'no reduced' well-being. It is possible to construct a classifier for severity of decrease in well-being that is in good agreement with a healthcare professional's opinion. That performance can be obtained with a relatively small number of questions. This allows implementation of a classifier in quick-to-complete questionnaires that can be used routinely.
    Original languageEnglish
    Title of host publicationProceedings of the 29th ICOH, International Congress on Occupational Health
    Publication statusPublished - 2009
    MoE publication typeNot Eligible
    Event29th ICOH, International Congress on Occupational Health - Cape Town, South Africa
    Duration: 22 Mar 200927 Mar 2009


    Conference29th ICOH, International Congress on Occupational Health
    Country/TerritorySouth Africa
    CityCape Town


    • personnel screening
    • well-being
    • neural networks


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