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Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

R. Punithavathi1,*, S. Thenmozhi2, R. Jothilakshmi3, V. Ellappan4, Islam Md Tahzib Ul5

1 M Kumarasamy College of Engineering, Karur, Tamil Nadu, India
2 Dayananda Sagar College of Engineering, Bangalore, Karnataka, India
3 R.M.D Engineering College, Kavaraipettai, Tamil Nadu, India
4 Adama Science and Technology University, Adama, Ethiopia
5 Dhaka International University, Dhaka, Bangaladesh

* Corresponding Author: R. Punithavathi. Email: email

Computer Systems Science and Engineering 2023, 45(1), 247-261.


During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition.


Cite This Article

R. Punithavathi, S. Thenmozhi, R. Jothilakshmi, V. Ellappan and I. M. T. Ul, "Suicide ideation detection of covid patients using machine learning algorithm," Computer Systems Science and Engineering, vol. 45, no.1, pp. 247–261, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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