ASIA unversity:Item 310904400/114985
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    题名: A bagging ensemble machine learning framework to predict overall cognitive function of schizophrenia patients with cognitive domains and tests
    作者: Lin, E;Lin, E;CH, Lin;CH, Lin;藍先元;Lane, Hsien Yuan
    贡献者: 醫學暨健康學院心理學系
    关键词: Cognitive domain:Cognitive test:Ensemble learning:Neurocognition:Schizophrenia:Social cognition
    日期: 2022-03-01
    上传时间: 2023-03-28 01:27:28 (UTC+0)
    出版者: 亞洲大學
    摘要: Background
    It has been indicated that the interplay between functional outcomes and cognitive functions in schizophrenia is arbitrated by clinical symptoms, where cognitive functions are evaluated by cognitive domains and cognitive tests.

    Methods
    To determine which single cognitive domain or test can best predict the overall cognitive function of schizophrenia, we established a bagging ensemble framework resulting from the analysis of factors such as 7 cognitive domain scores and 11 cognitive test scores of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, linear regression, support vector machine, and random forests.

    Results
    The analysis revealed that among the 7 cognitive domains, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. In addition, among the 11 cognitive tests, the visual learning and memory test can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. Finally, among the 7 cognitive domains and 11 cognitive tests, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework.

    Conclusion
    The study implicates that the bagging ensemble framework may provide an applicable approach to develop tools for forecasting overall cognitive function in schizophrenia using cognitive domains and/or cognitive tests.
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