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International Journal of Applied Research
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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

IMPACT FACTOR (RJIF): 8.4

Vol. 9, Issue 11, Part A (2023)

Demystification of mathematical modeling of anxiety disorder based on demographic factors in context of machine learning techniques

Demystification of mathematical modeling of anxiety disorder based on demographic factors in context of machine learning techniques

Author(s)
Madhvi Patwa and Dipti Pimputkar
Abstract
The mental risk poses a high threat to the individuals, especially overseas demographic, including expatriates in comparison to the general Arab demographic. This paper focuses on a comprehensive analysis of mental health problems such as depression, stress, anxiety, isolation, and other unfortunate conditions. The dataset is developed from a web-based survey. The detailed exploratory data analysis is conducted on the dataset collected from Tamil Nadoo to study an individual’s mental health and indicative help-seeking pointers based on their responses to specific pre-defined questions in a multicultural society. The proposed model validates the claims mathematically and uses different machine learning classifiers to identify individuals who are either currently or previously diagnosed with depression or demonstrate unintentional “save our souls” (SOS) behaviors for an early prediction to prevent risks of danger in life going forward. The accuracy is measured by comparing with the classifiers using several visualization tools. This analysis provides the claims and authentic sources for further research in the multicultural public medical sector and decision-making rules by the government.
Pages: 61-66  |  144 Views  75 Downloads
How to cite this article:
Madhvi Patwa, Dipti Pimputkar. Demystification of mathematical modeling of anxiety disorder based on demographic factors in context of machine learning techniques. Int J Appl Res 2023;9(11):61-66.
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