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Article

Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone

1
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
2
Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin 16995, Korea
3
Department of Psychiatry, The Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul 03722, Korea
4
Mobigen Co., 128, Beobwon-ro, Songpa-Gu, Seoul 05854, Korea
5
Korea Electronics Technology Institute, Seongnam-si 13509, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Joaquim Carreras
Healthcare 2022, 10(7), 1189; https://doi.org/10.3390/healthcare10071189
Received: 27 May 2022 / Revised: 23 June 2022 / Accepted: 23 June 2022 / Published: 24 June 2022
(This article belongs to the Special Issue Artificial Intelligence Applications in Medicine)
With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a “Mental Health Protector” application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system’s prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis. View Full-Text
Keywords: depressive symptoms feature; depression prediction; machine learning; smartphone depressive symptoms feature; depression prediction; machine learning; smartphone
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MDPI and ACS Style

Hong, J.; Kim, J.; Kim, S.; Oh, J.; Lee, D.; Lee, S.; Uh, J.; Yoon, J.; Choi, Y. Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone. Healthcare 2022, 10, 1189. https://doi.org/10.3390/healthcare10071189

AMA Style

Hong J, Kim J, Kim S, Oh J, Lee D, Lee S, Uh J, Yoon J, Choi Y. Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone. Healthcare. 2022; 10(7):1189. https://doi.org/10.3390/healthcare10071189

Chicago/Turabian Style

Hong, Juyoung, Jiwon Kim, Sunmi Kim, Jaewon Oh, Deokjong Lee, San Lee, Jinsun Uh, Juhong Yoon, and Yukyung Choi. 2022. "Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone" Healthcare 10, no. 7: 1189. https://doi.org/10.3390/healthcare10071189

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