Using Back-propagation Artificial Neural Network (BPN) and Discriminant Analysis (DA) to Classify the Functioning Level of Psychiatric Daycare Ward Patients' Activities of Daily Living

運用倒傳遞類神經網路與鑑別分析分類精神科日間病房病患日常生活功能

10.29478/TJP.201104.0006

施以諾(Yi-Nuo Shih)；謝弘一(Horng-I Hsieh)；王怡婷(I-Ting Wang)；李天行(Tian-Shyug Lee)；林宛儀(Wan-Yi Lin)

daycare ward ； daily living function ； computer-assisted assessment support system ； back-propagation neural network

台灣精神醫學

25卷1期（2011 / 04 / 01）

32 - 40

英文

Objectives: Evaluating daily functioning of people with mental illnesses with questionaires is time-consuming. Additionally, patients' familiarity with the context of function tests compromises test accuracy. Applying a computer-assisted support assessment system in this study, we intended to predict the daily functioning of the mentally ill objectively and conveniently. Methods: We collected 54 patients attending a psychiatric daycare ward at a medical center in the Taipei city. A five-fold cross-validation scheme was applied to minimize possible bias and to provide reliable estimates. We used discriminant analysis (DA) and a back-propagation neural network (BPN), to predict patients' daily functioning, according to gender, educational background, diagnosis, and age, on Chu's daily function scale. Results: Both models achieved high average overall accuracy of more than 70%. The BPN model had a high overall classification accuracy of 92.55%, 16.55% better than that of the DA model. Additionally, the discriminant function showed that young males not diagnosed with schizophrenia had better daily function. Conclusion: This study was found that the BPN as a computer-assisted assessment support system predicted daily functioning more effectively than DA. To predict daily functioning relatively more precisely, we suggest that future research need to expand the sample population and to use additional variables, such as patients' personality, family support, and living status.

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