Past Issues
Using Back-propagation Artifi cial Neural Network (BPN) and Discriminant
Analysis (DA) to Classify the Functioning Level of Psychiatric Daycare Ward
Patients’ Activities of Daily Living
Yi-Nuo Shih, Horng-I Hsieh, I-Ting Wang, Tian-Shyug Lee, Wan-Yi Lin
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
fi ve-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 classifi cation 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.
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
fi ve-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 classifi cation 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.
| Key Word | daycare ward, daily living function, computer-assisted assessment support system, back-propagation neural network |
|---|