International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 12, No. 4, 8 Aug. 2020
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Knowledge Extraction Methods as a Measurement Tool of Depression Discovery in Saudi Society
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Author(s)
Mohammed Abdullah Al-Hagery
1,*
Sara Saleh Alfaozan
1
Hajar Abdulrahman Alghofaily
1
Mohammed A. Hadwan
2
Index Terms
Depression, Saudi Arabia, Machine Learning, Data Mining, Support Vector Machine, Social Media
Abstract
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
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