000			02910nam a22001937a 4500
082			_a006.3 _bD993A
100			_aDwivedi, Sushil Kumar _945452
245			_aAI In The Prediction/Detection Of Mental Health Disorders _cby Sushil Kumar Dwivedi
260			_aIIT Jodhpur _bDepartment of Computer Science and Technology _c2023
300			_avii,21p. _bHB
500			_aMental health problems are a major concern these days. People take stress because of their nature of work, targets, achievements, night shifts, and overwork. They do not get enough time to go to the clinic and do a health checkup. There is no proper diagnosis method available for mental disorder detection. It has been observed that clinical diagnosis is expensive and a normal person cannot afford it. In busy life, people do not prefer to go to the clinic as it takes time and is often significantly delayed. In the last few years, social media data has been used in the context of eHealth and mental disorder diagnosis. Doctors use patients' posts and their comments on social media platforms to diagnose their infectious diseases. Multiple surveys were conducted on people, and data was collected on different queries asked during the survey. This survey data is used for better analysis to identify mental disorders in a person. Mental health problems are increasing day by day and need efficient medical care, which conducts investigations using machine learning models. This paper presents an application that takes input from users and validates whether the user has a mental disorder or not. This paper used survey data as train/test data and validated it using different machine learning algorithms. The model which returned maximum accuracy has been saved and used for prediction on the data supplied by the user in the application. Furthermore, we have done analysis on social media data for mental disorder prediction. We collected social media data from Twitter and Reddit and built machine learning models. These models were used as a base for predicting mental illness in a person who has a Twitter or Reddit account. The person enters his Twitter or Reddit account credentials and will come to know whether he has suicidal or depressive thoughts based on his last 100 comments or tweets. This last 100 comments setting is by default and can be changed to include more comments or tweets in the prediction. Users also have the flexibility to enter text and validate the model's correctness. There is scope to increase model accuracy by adding more data while building the model and adding more social media accounts in prediction and validation.
650			_aDepartment of Computer Science and Technology _945453
650			_aMental Health Diagnosis _945454
650			_aeHealth Applications _945455
650			_aMTech Theses _945456
700			_aKalra, Sumit _945457
700			_aDhaka, Suman _945458
942			_cTH
999			_c16590 _d16590