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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number647
JournalElectronics (Switzerland)
Volume9
Issue number4
DOIs
Publication statusPublished - Apr 2020

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Keywords

  • Multiple imputation by chained equations
  • Multistage support vector machine model
  • SVM-based recursive feature elimination
  • Unipolar depression

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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