Realizing an integrated multistage support vector machine model for augmented recognition of unipolar depression

Kathiravan Srinivasan, Nivedhitha Mahendran, Durai Raj Vincent, Chuan Yu Chang, Shabbir Syed-Abdul

研究成果: 雜誌貢獻文章

摘要

Unipolar depression (UD), also referred to as clinical depression, appears to be a widespread mental disorder around the world. Further, this is a vital state related to a person’s health that influences his/her daily routine. Besides, this state also influences the person’s frame of mind, behavior, and several body functionalities like sleep, appetite, and also it can cause a scenario where a person could harm himself/herself or others. In several cases, it becomes an arduous task to detect UD, since, it is a state of comorbidity. For that reason, this research proposes a more convenient approach for the physicians to detect the state of clinical depression at an initial phase using an integrated multistage support vector machine model. Initially, the dataset is preprocessed using multiple imputation by chained equations (MICE) technique. Then, for selecting the appropriate features, the support vector machine-based recursive feature elimination (SVM RFE) is deployed. Subsequently, the integrated multistage support vector machine classifier is built by employing the bagging random sampling technique. Finally, the experimental outcomes indicate that the proposed integrated multistage support vector machine model surpasses methods such as logistic regression, multilayer perceptron, random forest, and bagging SVM (majority voting), in terms of overall performance.

原文英語
文章編號647
期刊Electronics (Switzerland)
9
發行號4
DOIs
出版狀態已發佈 - 四月 2020

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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