Abstract
Statistical machine learning methods can provide help when developing preventative services and tools that support the empowerment of individuals. We explore how the self-organizing map could be utilized as a tool for analyzing, visualizing and browsing heterogeneous survey data on wellbeing that contains both quantitative (numeric) and qualitative (text) data. There is systematic evidence implying that social isolation has drastic consequences for subjective well-being and health. It is important to obtain a deeper understanding of the phenomenon. Analysis of loneliness questionnaire data (N=521) succeeds in identifying profiles of loneliness as well as identifies crowd-sourced ideas for improving social wellbeing among the different subgroups.
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Lagus, K., Saari, J., Nieminen, I.T., Honkela, T. (2013). Exploration of Loneliness Questionnaires Using the Self-Organising Map. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_51
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DOI: https://doi.org/10.1007/978-3-642-40728-4_51
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