Smote Synthetic Minority Oversampling Technique Bibtex Bibliography

Author

Abstract

Background: Technologies to identify human emotions from their voices have been recently developed and commercialized. The present study examines whether this type of technology can be applicable to the diagnosis of depression. Method: Approximately 2,000 participants were required to record their voices and answer PHQ-9 at three points (T1, T2, and T3) with a two month interval respectively. Seven parameters such as pitch, gain, and power were extracted from the available voice data. We estimated the diagnostic accuracy of depression from analyzing such voice parameters, defining a participant as suffering from depression when the PHQ-9 score was 10 or above. After combining the data derived at T1 and T2, we randomly extracted 70% of such combined data that were processed by means of the synthetic minority over-sampling technique (SMOTE) algorithm. Then, we generated potential models through ensemble learning in which three types of models (bagging, random forest, and boosting) competed with each other. The remaining 30% of the abovementioned data and data gained at T3 were combined and used for examination. In addition to sensitivity and specificity, the area under the curve (AUC) was used as the primary criteria for accuracy. Result: The random forest model showed the best estimation. Although AUC available from only the demographic data indicated moderate accuracy, AUC from only voice or both voice and demographic data showed high accuracy. However, AUC available from analysis by using T3 data did not show sufficient accuracy. Interpretation: Although voice cognition technology has high potential for diagnosis of depression, further innovation seems to be required.

Suggested Citation

  • SO Mirai & TAKEBAYASHI Yoshitake & SEKIZAWA Yoichi & SHIMOJI Takaaki, 2016. "Can We Diagnose Depression by Voices in Japanese? (Japanese)," Discussion Papers (Japanese) 16054, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:rdpsjp:16054

    Listed:
    • SO Mirai
    • TAKEBAYASHI Yoshitake
    • SEKIZAWA Yoichi
    • SHIMOJI Takaaki

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    Co-authors

    • Lawrence HallProfessor of Computer Science and Engineering, University of South FloridaVerified email at mail.usf.edu
    • Kevin W. BowyerSchubmehl-Prein Family Professor of Computer Science and Engineering, University of Notre DameVerified email at cse.nd.edu
    • David CieslakVP of Predictive ModelingVerified email at aunalytics.com
    • Karsten SteinhaeuserResearch Associate in Computer Science and Engineering, University of MinnesotaVerified email at umn.edu
    • Yuxiao DongMicrosoft ResearchVerified email at microsoft.com
    • Yang YangKellogg School of ManagementVerified email at kellogg.northwestern.edu
    • Auroop GangulyProfessor, Northeastern University, Boston, MAVerified email at neu.edu
    • Reid A. JohnsonComputer Science PhD, University of Notre DameVerified email at alumni.nd.edu
    • Tang JieAssociate Professor, Tsinghua UniversityVerified email at tsinghua.edu.cn
    • Yizhou SunAssistant Professor, Computer Science, UCLAVerified email at cs.ucla.edu
    • Jiawei HanAbel Bliss Professor of Computer Science, University of IllinoisVerified email at cs.uiuc.edu
    • David HachenProfessor of Sociology, University of Notre DameVerified email at nd.edu
    • Omar LizardoProfessor of Sociology, University of Notre DameVerified email at nd.edu
    • Jian Xu 徐健Ph.D., Computer Science and Engineering, University of Notre DameVerified email at nd.edu
    • Aaron StriegelAssociate Professor, Department of Computer Science and Engineering, University of Notre DameVerified email at nd.edu
    • Douglas ThainAssociate Professor, University of Notre DameVerified email at nd.edu

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