TY - JOUR
T1 - Realizing an integrated multistage support vector machine model for augmented recognition of unipolar depression
AU - Srinivasan, Kathiravan
AU - Mahendran, Nivedhitha
AU - Vincent, Durai Raj
AU - Chang, Chuan Yu
AU - Syed-Abdul, Shabbir
N1 - Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/4
Y1 - 2020/4
N2 - 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.
AB - 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.
KW - Multiple imputation by chained equations
KW - Multistage support vector machine model
KW - SVM-based recursive feature elimination
KW - Unipolar depression
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U2 - 10.3390/electronics9040647
DO - 10.3390/electronics9040647
M3 - Article
AN - SCOPUS:85083858536
SN - 2079-9292
VL - 9
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 4
M1 - 647
ER -