Abstract

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.

Highlights

  • 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

  • We found that the bagging ensemble model with feature selection using the selected features from clinical symptom scales performed best in predicting the QLS or GAF outcome when compared with other stateof-the-art algorithms, including multi-layer feedforward neural networks (MFNNs), support vector machine (SVM), linear regression, and random forests

  • This is the first study to date to identify synergistic effects between SANS20 and HAMD17 as well as between PANSS-Positive and SANS20 in influencing functional outcomes in schizophrenia among Taiwanese individuals using a bagging ensemble machine learning approach with the M5 Prime feature selection algorithm

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Summary

Introduction

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 utilized the same cohort of 302 patients with schizophrenia and carried out the first study on the QLS and GAF functional outcome prediction in schizophrenia patients with 3 clinical symptom scales and 11 cognitive function tests by using a bagging ensemble machine learning a­ pproach[25]. We hypothesized that our bagging ensemble machine learning method would be able to predict the QLS- and GAF-related outcome in patients with schizophrenia by using a small subset of selected clinical symptom scales and/or cognitive function assessments. We hypothesized that our bagging ensemble machine learning approach with the M5 Prime feature selection algorithm could lead to better performance

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