Obsessive-compulsive Disorder Patient’s Current Illness Stage Prediction Using Machine Learning Algorithms

Abstract

Nowadays mental health problems are increasing day by day. Among many mental health issues obsessive compulsive disorder is very common in this modern era. According to research out of 100 people 3 people have obsessive compulsive disorder. Though medical science has become so advanced but still today we are not that conscious about our mental health issues. People think that obsessive compulsive disorder is only about cleaning habits and it will be ok after a certain time. Obsessive compulsive disorder is not only a cleaning habit and it's about a lot of mental issues that a normal person who doesn’t have obsessive compulsive disorder cant even relate to. People having obsessive compulsive disorder also don’t know which level of their disease they are currently. As like other physical health diseases mental health disease also has some state of condition like ( primary, mid, higher or extreme). “Prediction of current status of obsessive compulsive disorder “ system based on predictive modeling predicts the disease of the user on the basis of the symptoms that the user provides as an input to the system. The system analyzes the symptoms provided by the user as input and gives the probability of the current status of obsessive compulsive disorder as an output. “prediction of current status of obsessive compulsive disorder “ is done by implementing many techniques such as Naïve Bayes, KNN, Decision Tree, Linear Regression and Random Forest Algorithms etc . These techniques calculate the probability current status of the disease. Therefore, average prediction accuracy probability 53% is obtained.