Predicting Mental Disorders Using Data Mining Techniques

Abstract

Over the last few years, data mining makes a transition to new applications in medicine. Many people suffer from mental disorders because of absence of care in the health care sector, especially in the mental care. Lack of health records for patients of mental health, has been caused by many reasons, for example, patients’ hesitation to go the psychiatrist to follow-up their condition due high cost of examination fee, waiting time for their turn, and diagnosis inaccuracy. Classifiers are used to predict normal, addiction without disorder, addiction with disorder, or disorder without addiction patients’ cases. However, a rise in the cases of personality disorder and substance abuse. We used Naive Bayes and K-Nearest Neighbor classification technique on dataset consists of opioid addicts patients derived from the United States of America to obtain high accuracy of mental disorder prediction. The precision of the NB classifier is (87%), and the precision of KNN classifier is (92%). This paper presents a new method for diagnosing mental disorders through data mining.