AI In The Prediction/Detection Of Mental Health Disorders
Dwivedi, Sushil Kumar
AI In The Prediction/Detection Of Mental Health Disorders by Sushil Kumar Dwivedi - IIT Jodhpur Department of Computer Science and Technology 2023 - vii,21p. HB
Mental 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.
Department of Computer Science and Technology
Mental Health Diagnosis
eHealth Applications
MTech Theses
006.3 / D993A
AI In The Prediction/Detection Of Mental Health Disorders by Sushil Kumar Dwivedi - IIT Jodhpur Department of Computer Science and Technology 2023 - vii,21p. HB
Mental 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.
Department of Computer Science and Technology
Mental Health Diagnosis
eHealth Applications
MTech Theses
006.3 / D993A