Unsupervised neural network for evaluating the ability of the SF-36 instrument to differentiate individuals.

ABSTRACT

BACKGROUND: Health-related quality of life (HRQoL) and well-being refer to the positive, subjective state that is contrary to illness. HRQoL instruments include some common questionnaires, which may often be understood differently depending on the level of individuals' knowledge. AIMS: To investigate the ability of 36 Short Form Health Survey (SF-36) as a well-known questionnaire in evaluating people's well-being. METHODS: We compared unsupervised artificial neural networks with a self-organized map learning algorithm and k-means clustering method. Understanding of the content of the questionnaire was also checked according to age group and sex. The study included 1087 people aged > 18 years (640 healthy individuals and 447 patients with chronic diseases) in Shiraz, Islamic Republic of Iran between 2011 and 2013. RESULTS: The eight subscale scores of the SF-36 instrument were not able to evaluate the well-being of people. The ability of all 36 items in the questionnaire was > 60% in both self-organized map and k-means methods. The self-organized map learning algorithm evaluated people better than the k-means clustering method, based on the accuracy rate in prediction. The SF-36 instrument was better understood by young people. CONCLUSIONS: Differences in people's health conditions may not appear on the SF-36 subscale scores; therefore, the findings from the subscale scores of SF-36 should be cautiously interpreted.