Anxiety Prediction and Analysis- A Machine Learning Based Approach

Anxiety is considered a psychological disorder that causes stress, emotional trauma, and so forth, which affects a person both mentally and physically. This condition causes a person to experience fear, helplessness, a genetic predisposition, homelessness and unemployment, alcohol and other drug usages, and traumatic life events. To avoid the risk factors, it's necessary to diagnose the illness earlier. There are several symptoms we can look for to determine if someone has an anxiety disorder or not. We converted all the symptoms into parameters, which are further used for training test data and model fitting. And to validate it further, a real-time dataset taken from Kashmir is being used. As machine learning has many models by which anyone can predict different outputs, in this study, we use a different machine learning model to predict the output (whether the person is suffering from anxiety or not). This study is based on a classification problem in which we compare different models and then make a comparison table of them. In short, our study uses the result that comes out after screening a person to predict anxiety. The model is fitted in a very simple way so that any patient or person can predict whether he or she is affected by anxiety or not with an accuracy of 95%.