Abstract
The mental health or well-being of an individual is described as his/her state of mind which conjointly provides an outline of that individual’s nature. It is primarily the combination of psychological, emotionality, and well-being of an individual socially. The ability of a person to think, feel, and handle situations determines his mental health. An ample of factors result in prior mental illness, for example, stress, depression, anxiety with simultaneous obsessive-compulsive disorder and moreover personality disorders. Right now, we are all facing emotions, thoughts, and situations that we have never been through. In India, the COVID-19 pandemic scenario is having a huge and significant effect on public mental health with regards to their sex, age, profession, socio-economic status, their residing place, etc. The frontline workers are more distressed than the other professionals; the plight of migrants is disturbing; unemployment of huge numbers of people, students, and teachers facing distress as some are unable to afford online platforms and smooth transition to online learning. Therefore, monitoring the mental health of the population during this critical period is an immediate priority. Machine learning algorithm and the pure nature of artificial intelligence (AI) can be used to predict the onset of mental illness. AI is a revolutionary and wide-ranging field of computer science that is involved with performing several tasks that substitute human intelligence by building smart and computational tools and machines. Over the coming years and decades, it has set to become a core component of all modern software. Machine learning is a subset of AI. This research work has employed the application of various machine learning algorithms on the Jupyter platform, such as the k-nearest neighbors (KNN) algorithm and seaborn to determine the state of mental illness in particular target groups. Using these above-mentioned tools, we have generated few graphs that show the stress and depression counts among different age groups. Analyzing the results so obtained in this research paper, we can clearly figure out the appropriate measures that can be taken into consideration for any such dilemma in the near future.
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Majumder, A., Arora, M.S., Mantri, P., Saxena, A. (2022). Increase in Mental Health Cases Post COVID Outbreak. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1388. Springer, Singapore. https://doi.org/10.1007/978-981-16-2597-8_3
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