Detecting Depression Using K-Nearest Neighbors (KNN) Classification Technique

@article{Islam2018DetectingDU,
  title={Detecting Depression Using K-Nearest Neighbors (KNN) Classification Technique},
  author={Md. Rafiqul Islam and A. Kamal and N. Sultana and Robiul Islam and M. Moni and Anwaar Ulhaq},
  journal={2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2)},
  year={2018},
  pages={1-4}
}
  • Md. Rafiqul Islam, A. Kamal, +3 authors Anwaar Ulhaq
  • Published 2018
  • Computer Science
  • 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2)
Social networks have developed as a promising point for everybody to communicate with their interested friend and share their opinions, photos, and videos. [...] Key Result We do believe that our investigation and approach might be helpful to raise consciousness in online social network users.Expand
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References

SHOWING 1-10 OF 15 REFERENCES
Predicting Depression Levels Using Social Media Posts
Predicting Depression via Social Media
Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users
Affective and Content Analysis of Online Depression Communities
Predicting postpartum changes in emotion and behavior via social media
Detecting Emotions in Social Media: A Constrained Optimization Approach
Predicting tie strength with social media
"Facebook depression?" social networking site use and depression in older adolescents.
...
1
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