Abstract

Psychological disorder is a caused due to continuous feeling of sadness and not a single hope from outside world. This feeling negatively effects on mental and emotional wellbeing of patients. Psychological disorders are a serious and growing global health concern [1]. Psychological disorders patients have become a leading key player to the global health crisis. Traditional diagnostic approaches are often time-consuming and can be affected by factors such as patient unwilling to self-report symptoms owing to social shame. In today’s many people undergone psychological disorder so it is essential to detect the psychological disorder. In this paper The Machine learning (ML) algorithm was used to get a potential solution by using digital data for early detection and interventions of psychological disorders. This paper also focuses development of Naïve Bayes model for classification mental health disorders. In this model a text-based data corpora have been used which was collected from the Kaggle and a Naïve Bayes (NB) algorithm is also used for the classification of mental health disorders. This model gives 88% accuracy to detect the mental health disorders like Anxiety, and Depression.

References

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