Efficient Approaches to Predict Neurological Disorder using Social Networking Sites

With the extensive utilization of Social Networking Sites (SNS), users tend to disclose their feelings through various posts, images, emoticons posted over SNS profiles. Here, different information diagnostic ways are gently analyzed to predict neurological disorder through cognitive-behavioral approach, sentiment analysis, and machine learning techniques using earlier publications. It is observed that Machine learning approach gives higher accuracy and better results than Cognitive behavioral therapy and Sentiment Analysis. Also, it is being noticed that the techniques like Support Vector machine (SVM), K Fold validation, Logistic Regression, Decision Tree, and Random Forest are mostly used for the prediction of emotional disclosure. It is being noticed that depression is most common mental illness found among individuals along with some signs of Stress, anxiety, bipolar disorder or even suicidal ideation. However, all techniques are more versatile and robust but SVM works out the best amongst all.