An Automation for Mental Health Analysis of College Students

Abstract: Somatization, depression, anxiety, fear, paranoid, interpersonal sensitivity and psychosis are some of the mental health problems that the college students are enduring from. These problems bring many negative effects to them. For analysis the relationship between these mental health problems from the dataset, many association rule mining algorithms are already used. These algorithms concentrate on positive rules and they don’t concentrate on negative rules. So this particular paper focuses to mine both negative and positive rules from the mental health dataset of college students. Here the mental health dataset of college students is considered and by using association rules, the correlation between different mental health problems is predicted using this dataset.

Keywords: Association Rule, Positive Rules, Negative Rules, Apiori Algorithm.