Predicting Depressive Behavior with Monitoring Activity Data Using Machine Learning and Feature Selection Approaches-Reference-Cited by-同舟云学术

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Predicting Depressive Behavior with Monitoring Activity Data Using Machine Learning and Feature Selection Approaches

Published:2024-05-02 Issue: Volume:10 Page:284-289
ISSN:
Container-title:2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)
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Author:

Sarder Md. Hosain¹,Raihan M.¹,Debnath Anjan²,Islam Md. Shymon¹,Samrat Nadim³,Efty Riyana Islam¹,Hasan Md. Mahedi¹,Fahimuzzaman Md.¹

Affiliation:

1. North Western University,Computer Science and Engineering,Khulna,Bangladesh,9100

2. Khulna University of Engineering & Technology,Computer Science and Engineering,Khulna,Bangladesh,9203

3. Khulna University,Computer Science and Engineering,Khulna,Bangladesh,9208

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10534252/10534311/10534476.pdf?arnumber=10534476

Reference23 articles.

1. 47. Computational Models of Effort-Based Choice in Patients With Major Depression and Schizophrenia

2. A Data Mining Approach to Identify the Stress Level Based on Different Activities of Human

3. Depression facts and statistics,2024

4. Comparison of Night, Day and 24 h Motor Activity Data for the Classification of Depressive Episodes

5. Ecological Momentary Assessment

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