Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society

Full Text (PDF, 699KB), PP.1-10

Views: 0 Downloads: 0


Mohammed Abdullah Al-Hagery 1,* Sara Saleh Alfaozan 1 Hajar Abdulrahman Alghofaily 1 Mohammed A. Hadwan 2

1. Department of Computer Science, College of Computer, Qassim University, Buraidah, Saudi Arabia

2. Department of IT, College of Computer, Qassim University, Buraidah, Saudi Arabia

* Corresponding author.


Received: 11 Dec. 2019 / Revised: 3 Feb. 2020 / Accepted: 14 Feb. 2020 / Published: 8 Aug. 2020

Index Terms

Depression, Saudi Arabia, Machine Learning, Data Mining, Support Vector Machine, Social Media


Depression is a widespread and serious phenomenon in public health in all societies. In Saudi society, depression is one of the diseases that the community is may refuse to disclose it. There are no studies have analyzed this disease within the Saudi community. The main research objective is to discover the depression level of Saudi People's. In addition to analyzing the age group and the most gender type affected by the depression in this society. The data collected from social media achieved indirectly without any communication with patients as a sample from this society people. It analyzed using Machine Learning algorithms that give accurate results for this disease. Three classification models have been established to diagnose this disease and the findings of this study presented that the depression levels include five ‎classes and ‎the most affected age group in depression was in the ‎age group from 20-26 years. The results show that young Saudi women are more likely to be depressed. The obtained results are very important to the medical field. Researchers and people working in this field can get benefits out of this research. Especially those who want to understand the depression disease in Saudi society and searching for real solutions to overcome this problem.

Cite This Paper

Mohammed Abdullah Al-Hagery, Sara Saleh Alfaozan, Hajar Abdulrahman Alghofaily, Mohammed A. Hadwan, "Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society", International Journal of Information Technology and Computer Science(IJITCS), Vol.12, No.4, pp.1-10, 2020. DOI:10.5815/ijitcs.2020.04.01


[1]J. Dine and J. Dine, “A global crisis?,” Companies, Int. Trade Hum. Rights, pp. 1–40, 2010.

[2]Z. Layer, K.; Khan, “depression – A Review Review Paper depression – A Review,” Res. J. Recent Sci., vol. 1, no. 4, pp. 79–87, 2015.

[3]M. Illness and P. Disorders, “Mental Illness/ Psychological Disorders - depression,” pp. 9–11.

[4]C. J. Murray and A. D. Lopez, “Global Burden of Disease and Injur Y Series the Global Burden of Disease,” Oms, pp. 1–46, 1996. 

[5]H. F. Sandmire, S. D. Austin, and R. C. Bechtel, “Experience with 40,000 Papanicolaou smears,” Obstet. Gynecol., vol. 48, no. 1, pp. 56–60, 1976.

[6]O. A. Alibrahim, N. Al-Sadat, and N. A. M. Elawad, “Gender and risk of depression in Saudi Arabia, a systematic review and meta-analysis,” J. Public Health Africa, vol. 1, no. 1, pp. 22–25, 2010.

[7]M. Peyrot and R. R. Rubin, “Levels and risks of depression and anxiety symptomatology among diabetic adults,” Diabetes Care, vol. 20, no. 4, pp. 585–590, 1997.

[8]E. H. Lee, S. J. Lee, S. T. Hwang, S. H. Hong, and J. H. Kim, “Reliability and validity of the beck depression inventory-II among Korean adolescents,” Psychiatry Investig., vol. 14, no. 1, pp. 30–36, 2017.

[9]K. K. Strunk and F. C. Lane, “The Beck depression Inventory, Second Edition (BDI-II),” Meas. Eval. Couns. Dev., p. 074817561666401, 2016.

[10]I. H. Gotlib, P. M. Lewinsohn, and J. R. Seeley, “Symptoms versus a diagnosis of depression: Differences in psychosocial functioning,” J. Consult. Clin. Psychol., vol. 63, no. 1, pp. 90–100, 1995.

[11]R. S. Lazarus, “Coping theory and research: Past, present, and future,” Psychosom. Med., vol. 55, no. 3, pp. 234–247, 1993.

[12]C. Hammen et al., “NIH Public Access,” vol. 26, no. 8, pp. 718–723, 2012.

[13]J. M. F. Hall, D. Cruser, A. Podawiltz, D. I. Mummert, H. Jones, and M. E. Mummert, “Psychological stress and the cutaneous immune response: Roles of the HPA Axis and the sympathetic nervous system in atopic dermatitis and psoriasis,” Dermatol. Res. Pract., vol. 2012, no. August, 2012.

[14]S. P. Lee, I. K. Sung, J. H. Kim, S. Y. Lee, H. S. Park, and C. S. Shim, “The effect of emotional stress and depression on the prevalence of digestive diseases,” J. Neurogastroenterol. Motil., vol. 21, no. 2, pp. 273–282, 2015.

[15]K. Weitkamp, E. Klein, and N. Midgley, “The Experience of depression: A Qualitative Study of Adolescents With depression Entering Psychotherapy,” Glob. Qual. Nurs. Res., vol. 3, 2016.

[16]Y. Ling, C. Liu, E. Scott Huebner, Y. Zeng, N. Zhao, and Z. Li, “A study on classification features of depressive symptoms in adolescents.,” J. Ment. Health, vol. 0, no. 0, pp. 1–8, 2019.

[17]M. Shields, “Stress and depression in the employed population,” no. September, 2014.

[18]M. Turhan, S. Karabatak, D. Şengür, and M. Zincirli, “Managerial Resourcefulness in School Administrators: Association with Stress and depression,” Cukurova Univ. Fac. Educ. J., vol. 47, no. 1, pp. 216–232, 2018.

[19]S. Burak and O. Atabek, “Association of Career Satisfaction with Stress and depression: The Case of Preservice Music Teachers,” J. Educ. Learn., vol. 8, no. 5, p. 125, 2019.

[20]V. S. Minchekar, “Academic stress and depression among college students,” Int. J. Curr. Res., vol. 10, no. 12, pp. 76429–76433, 2018.

[21]A. B. Negrão and P. W. Gold, “Major Depressive Disorder,” Encycl. Stress, vol. 28, pp. 640–645, 2007.

[22]J. D. Leander and D. E. McMillan, “Schedule induced narcotic ingestion,” Pharmacol. Rev., vol. 27, no. 4, pp. 475–487, 1975.

[23]O. Gunay, F. Akpinar, S. Poyrazoglu, and H. Aslaner, “Original Article Prevalence of depression among Turkish University Students and related factors Üniversite öğrencilerinde depresyon prevalansı ve ilişkili faktörler,” vol. 9, no. November 2010, pp. 133–143, 2011.

[24]L. A. Dardas et al., “Comparison of the performance of the Beck depression Inventory-II and the Center for Epidemiologic Studies-depression Scale in Arab adolescents,” Public Health Nurs., vol. 36, no. 4, pp. 564–574, 2019.

[25]E. Şahin and C. Cerit, “A Research of Posttraumatic Stress and depression Symptoms in Emergency Ambulance Staff,” Kocaeli Üniversitesi Sağlık Bilim. Derg., vol. 5, no. 3, pp. 156–160, 2019.

[26]N. H. Cha and S. R. Sok, “depression, self-esteem and anger expression patterns of Korean nursing students,” Int. Nurs. Rev., vol. 61, no. 1, pp. 109–115, 2014.

[27]D. Ignjatović-Ristić, D. Hinić, and J. Jović, “Evaluation of the beck depression inventory in a nonclinical student sample,” West Indian Med. J., vol. 61, no. 5, pp. 489–493, 2012.

[28]S. Nasıroğlu and V. Çeri, “Posttraumatic stress and depression in Yazidi refugees,” Neuropsychiatr. Dis. Treat., vol. 12, no. December 2017, pp. 2941–2948, 2016.

[29]A. R. A. Asal and M. M. Abdel-Fattah, “Prevalence, symptomatology, and risk factors for depression among high school students in Saudi Arabia,” Neurosciences, vol. 12, no. 1, pp. 8–16, 2007.

[30]A. H. Khalil, M. A. Rabie, M. F. Abd-El-Aziz, T. A. Abdou, A. H. El-Rasheed, and W. M. Sabry, “Clinical characteristics of depression among adolescent females: A cross-sectional study,” Child Adolesc. Psychiatry Ment. Health, vol. 4, no. 1, p. 26, 2010.

[31]L. A. Dardas, N. Shoqirat, H. Abu-hassan, B. F. Shanti, A. Al-khayat, and D. H. Allen, “depression in Arab Adolescents,” vol. 57, no. 10, pp. 34–43.

[32]H. A. Razzak, A. Harbi, and S. Ahli, “depression: Prevalence and associated risk factors in the United Arab Emirates,” Oman Med. J., vol. 34, no. 4, pp. 274–283, 2019.

[33]T. R. S. Mary and S. Sebastian, “Predicting heart ailment in patients with varying number of features using data mining techniques,” Int. J. Informatics Commun. Technol., vol. 8, no. 1, p. 56, 2019.

[34]M. Ramageri, “Data Mining Techniques and Applications,” Indian J. Comput. Sci. Eng., vol. 1, no. 4, pp. 301–305, 2010.

[35]W. M. Gibson et al., “Convergent-beam neutron crystallography,” J. Appl. Crystallogr., vol. 37, no. 5, pp. 778–785, 2004.

[36]A. R. Kulkarni and D. S. D. Mundhe, “Data Mining Technique: An Implementation of Association Rule Mining in Healthcare,” Iarjset, vol. 4, no. 7, pp. 62–65, 2017.

[37]A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z. Med. Phys., vol. 29, no. 2, pp. 102–127, 2019.

[38]D. P., “A Few Useful Things to Know About Machine Learning,” Commun. ACM, vol. 55, no. 10, 2012.

[39]H. Kaur and V. Kumari, “Predictive modelling and analytics for diabetes using a machine learning approach,” Appl. Comput. Informatics, no. February, 2019.

[40]L. Zhao, F. Hao, T. Xu, and X. J. Dong, “Positive and negative association rules mining for mental health analysis of college students,” Eurasia J. Math. Sci. Technol. Educ., vol. 13, no. 8, pp. 5577–5587, 2017.

[41]X. Xu et al., “Leveraging Routine Behavior and Contextually-Filtered Features for depression Detection among College Students,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 3, no. 3, pp. 1–33, 2019.

[42]M. Morales, S. Scherer, and R. Levitan, “A Cross-modal Review of Indicators for depression Detection Systems,” no. January, pp. 1–12, 2017.

[43]S. Kumar, B. Bhattacharyya, and V. K. Gupta, “Present and Future Energy Scenario in India,” J. Inst. Eng. Ser. B, vol. 95, no. 3, pp. 247–254, 2014.

[44]Y. Guo, X. Yin, X. Zhao, D. Yang, and Y. Bai, “Hyperspectral image classification with SVM and guided filter,” Eurasip J. Wirel. Commun. Netw., vol. 2019, no. 1, 2019.

[45]S. V. and M. M., “Review: Sentiment Analysis using SVM Classification Approach,” Int. J. Comput. Appl., vol. 181, no. 37, pp. 1–8, 2019.

[46]C. Anne, A. Mishra, M. T. Hoque, and S. Tu, “Multiclass patent document classification,” Artif. Intell. Res., vol. 7, no. 1, p. 1, 2017.

[47]M. Gamon and S. Counts, “Letter for comprehensive pediatric nursing,” Compr. Child Adolesc. Nurs., vol. 36, no. 1–2, pp. 168–169, 2013.

[48]A. R. Bagasta, Z. Rustam, J. Pandelaki, and W. A. Nugroho, “Comparison of Cubic SVM with Gaussian SVM: Classification of Infarction for detecting Ischemic Stroke,” IOP Conf. Ser. Mater. Sci. Eng., vol. 546, no. 5, 2019.

[49]A. Famili, W. M. Shen, R. Weber, and E. Simoudis, “Data preprocessing and intelligent data analysis,” Intell. Data Anal., vol. 1, no. 1, pp. 3–23, 1997.

[50]Y. Liu and J. Du, “Parameter Optimization of the SVM for Big Data,” Proc. - 2015 8th Int. Symp. Comput. Intell. Des. Isc. 2015, vol. 2, no. 1, pp. 341–344, 2016.

[51]M. H. Dunham, “Introductory and Advanced Topics Part I,” pp. 1–21, 2002.