System Engineering and Productivity

System Engineering and Productivity

Data Mining of Depressed Patients to Improve and Examine its Relationship with Music

Document Type : Research Paper

Authors
Mahtab Jamaly 1
Zohre Faraji 2
Mohammad Rabiei 3
1 Corresponding author: M.Sc., Department of Computer Engineering, Faculty of Electrical, Computer Engineering, Ghiaseddin Jamshid Kashani University, Qazvin, Iran
2 M.Sc., Department of Computer Engineering, Faculty of Electrical, Computer Engineering, Ghiaseddin Jamshid Kashani University, Qazvin, Iran
3 Assistant Professor, Department of Computer Engineering, Faculty of Electrical, Computer and Mechanical Engineering, University of Eyvanekey, Eyvanekey, Iran
10.22034/sep.2022.243416
Abstract
Nowadays, collecting data on diseases is of great importance for their identification and treatment. Data mining methods can be used to discover hidden patterns in this data. The results of data mining help doctors find new solutions for treating or preventing diseases. Depression is one of the mental illnesses that is spreading day by day. This disease is accompanied by mood, thought, and body disorders and makes the person feel sad and useless. The main goal of this study is to provide a model to diagnose the level of depression in depressed patients and to examine its relationship with music in order to provide useful solutions for the improvement of these patients. In this study, 470 patients with depression in the cities of Tehran and Karaj were examined to discover the relationship between lifestyle parameters and their favorite music with depression. Various physical diseases can lead to depression. Therefore, a number of these diseases were investigated in this study. Decision tree and support vector machine algorithms and the Rapidminer program were used. The results showed that music and exercise play an important role in the level of depression in people, and listening to sad, rock, and metal music can help a person become depressed.
Keywords
Depression
Data mining
Music
Decision tree
Support vector machine

Copyright ©, Mahtab Jamaly, Zohre Faraji, Mohammad Rabiei

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Akthar F, Hahne C.RapidMiner 5 Operator Reference.2012. Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R,Ghandeharioun A, Bahadorian B, Sani ZA. A data mining approach for diagnosis of coronary artery disease. 2013. Comput Methods Programs Biomed 111:52–61. [PubMed: 23537611].
Caddy C, Amit B.H, McCloud T.L et al. Ketamine and other glutamate receptor modulators for depression in adults. 2015. Cochrane Database of Systematic Reviews. no. 9. article CD011612.
ChaitraliDangare S, SulabaApte S. Improved study of disease prediction using data mining classification techmiques. 2010. Int.J.Comp.Appl.Chen L, Zhou S, Bryant J. Temporal changes in mood repair through music consumption: Effects of mood, mood salience, and individual differences.2007. Media Psychology.
Corporation T C .Introduction to Data Mining and Knowledge Disocvery.2005
Daimi K, Banitaan S. Using Data Mining to Predict Possible Future Depression Cases. 2014. Vol.3. No.4.
Dipnall Joanna F, Pasco Julie A and Berk M. Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression. 2016.
Esfandiari N, Mansouri S. The effect of listening to light and heavy Music on reducing the symptoms of depression among female students. 2014. Arts Psychother. 41. 211–213.
FaiChan M, EstherMok. Effects of music on depression and sleep quality in elderly people: A randomised controlled trial. 2010. Complementary Therapiesin Medicine.
Halaris A. A primary focus on the diagnosis and treatment of major depressive disorder in adults. 2011.
Han J, Kamber M, Pei J. Data mining: concepts and techniques: Morgan kaufmann. 2006.
Hossein zadeh S. Data mining and its application to health at the first specialized conference on electricity and computers. 1363. No.4. In persian
Huang Yu-Jhen, Hsien-Yuan Lane, and Chieh-Hsin Lin. New Treatment Strategies of Depression: Based on Mechanisms Related to Neuroplasticity.2017. Article ID 4605971. 11 pages.
Kharya S. Using data mining techniques for diagnosis and prognosis of cancer disease.2012. Int. J. Comput. Sci. Inf. Technol. vol. 2. no. 2. pp. 55–66.
Leubner D, Hinterberger T. Reviewing the Effectiveness of Music Interventions in Treating Depression. 2017.
Malekpour S, Esaeilpour M. Investigating Factors Affecting Depression in People Using Data Mining Methods.1396. In Persian
Mathew RJ, Largen J, Claghorn JL. Biological Symptoms of Depression. 1979. Psychosomatic Medicine. vol/issue: 41(6). pp. 439-443.
Milovic B, Milovic M. Prediction and Decision Making in Health Care using Data Mining. 2012. International Journal of Public Health Science (IJPHS).vol/issue: 1(2). pp. 69-78.
Milovic B, Milovic M. Prediction and Decision Making in Health Care using Data Mining. 2012. International Journal of Public Health Science (IJPHS).vol/issue: 1(2). pp. 69-78.
Mohammadi M, Al-Azab F, Raahemi B. Data mining EEG signals in depression for their diagnostic value. 2015
Perez S, Perez V. Affects of music therapy on depression compared with psychotherapy. 2010. The Arts in Psychotherapy 37. 387–390.
Taneja A. Heart Disease Prediction System Using Data Mining Techniques.2013. Yoon S, Bakken S. Using a Data Mining Approach to Discover Behavior Correlates of Chronic Disease: A Case Study of Depression.2014. Stud Health Technol Inform.
System Engineering and Productivity
Volume 2, Issue 3 - Serial Number 4
Serial No. 4, Autumn Quarterly
Autumn 2022
Pages 49-73

  • Receive Date 24 October 2022
  • Revise Date 16 November 2022
  • Accept Date 19 November 2022
  • First Publish Date 19 November 2022
  • Publish Date 22 November 2022