Machine Learning Framework for Detection of Psychological Disorders at OSN
Punam B. Nalinde1, Anita Shinde2

1Miss. Punam B. Nalinde, ME Student, Computer Department, MMCOE College, Pune, India. 
2Prof. Anita Shinde, Associate Professor, Computer Department, MMCOE College, Pune, India
Manuscript received on 22 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 3293-3298 | Volume-8 Issue-11, September 2019. | Retrieval Number: I8823078919/2019©BEIESP | DOI: 10.35940/ijitee.I8823.0981119
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Abstract: Now a days attractiveness of social networking sites indications to the problematic habit. For this reason, researchers devised stress detection systems based psychological disorders in social networks. In this work, we propose a system of psychological disorders detection (PDD) that can provide online social behaviour extraction. It offers an opportunity to identify disorder at an early stage. These PDD system are made a different and advanced for the preparation of disorder detection. Propose system a machine learning approach that is detection of psychological disorders in social networks and social interaction features from social network data for detect with precision possible cases of disorders detection. We perform an analysis of the characteristics, and we also apply machine learning classifier in large-scale data sets and analyse features of psychological mental disorders. After classification results show that user are in stress or not, will be detected by PDD system is used to recommend hospitals on a map and at the same time admin will send mail of precaution list to user for users healthy and happy in life. The proposed method could help in developing a social network diagnostic tool for stress detection. It is useful in the diagnosis of psychological disorder detection in social platforms.
Keywords: PDD (Psychological Disorder Detection), OSN (Online Social Network), SNMD (Social Network Mental Disorder) Classifier, feature extraction.
Scope of the Article: Machine Learning