PMID- 33767310 OWN - NLM STAT- MEDLINE DCOM- 20211027 LR - 20211027 IS - 2045-2322 (Electronic) IS - 2045-2322 (Linking) VI - 11 IP - 1 DP - 2021 Mar 25 TI - Applying a bagging ensemble machine learning approach to predict functional outcome of schizophrenia with clinical symptoms and cognitive functions. PG - 6922 LID - 10.1038/s41598-021-86382-0 [doi] LID - 6922 AB - It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF) Scale. To determine the outcome assessed by QLS and GAF, we established a bagging ensemble framework with a feature selection algorithm resulting from the analysis of factors such as 3 clinical symptom scales and 11 cognitive function scores of 302 patients with schizophrenia in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, support vector machine, linear regression, and random forests. The analysis revealed that the bagging ensemble model with feature selection performed best among predictive models in predicting the QLS functional outcome by using 20-item Scale for the Assessment of Negative Symptoms (SANS20) and 17-item Hamilton Depression Rating Scale (HAMD17). Moreover, to predict the GAF outcome, the bagging ensemble model with feature selection performed best among predictive models by using SANS20 and the Positive and Negative Syndrome Scale-Positive (PANSS-Positive) subscale. The study indicates that there are synergistic effects between negative (SANS20) and depressive (HAMD17) symptoms as well as between negative and positive (PANSS-Positive) symptoms in influencing functional outcome of schizophrenia using the bagging ensemble framework with feature selection. FAU - Lin, Eugene AU - Lin E AD - Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA. AD - Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, 98195, USA. AD - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. FAU - Lin, Chieh-Hsin AU - Lin CH AD - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. cyndi36@gmail.com. AD - Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan. cyndi36@gmail.com. AD - School of Medicine, Chang Gung University, Taoyuan, Taiwan. cyndi36@gmail.com. FAU - Lane, Hsien-Yuan AU - Lane HY AD - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan. hylane@gmail.com. AD - Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan. hylane@gmail.com. AD - Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan. hylane@gmail.com. AD - Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan. hylane@gmail.com. LA - eng PT - Comparative Study PT - Journal Article PT - Research Support, Non-U.S. Gov't PT - Validation Study DEP - 20210325 PL - England TA - Sci Rep JT - Scientific reports JID - 101563288 SB - IM MH - *Cognition MH - Humans MH - *Machine Learning MH - *Psychosocial Functioning MH - Quality of Life MH - *Schizophrenia MH - *Schizophrenic Psychology MH - Support Vector Machine PMC - PMC7994315 COIS- The authors declare no competing interests. EDAT- 2021/03/27 06:00 MHDA- 2021/10/28 06:00 PMCR- 2021/03/25 CRDT- 2021/03/26 06:41 PHST- 2020/11/13 00:00 [received] PHST- 2021/03/08 00:00 [accepted] PHST- 2021/03/26 06:41 [entrez] PHST- 2021/03/27 06:00 [pubmed] PHST- 2021/10/28 06:00 [medline] PHST- 2021/03/25 00:00 [pmc-release] AID - 10.1038/s41598-021-86382-0 [pii] AID - 86382 [pii] AID - 10.1038/s41598-021-86382-0 [doi] PST - epublish SO - Sci Rep. 2021 Mar 25;11(1):6922. doi: 10.1038/s41598-021-86382-0.