Abstract:
Obsessive-compulsive disorder (OCD) is a common mental health problem
described by obsessive thoughts and compulsive behaviors. Obsessions are
intrusive, unpleasant thoughts, ideas, or desires that cause excruciating
feelings. Compulsions are behaviors that an individual participates in to try
to eliminate obsessions and reduce suffering. Everyone experiences
obsessions and compulsions at some point. The majority of the Sri Lankan
undergraduates have OCD but they are unaware of it. According to
psychologists, contamination & washing, doubt and checking ordering, and
arranging are the most common characteristics shown by students. If the
student is aware that they have OCD, they can act to avoid it. These
unpleasant thoughts and acts become more irritating as they interfere with
students' learning. It also increases students' anxiety and depression. The
study aims to develop a computerized model based on a data mining strategy
to identify the pervasiveness of Obsessive-Compulsive Disorder (OCD)
among Sri Lankan Undergraduates. This model identifies whether the
students suffer from OCD as Low Moderate or High Level. Before the
development of the model, a formal questionnaire was developed to examine
the behaviors of undergraduates, and data was collected from Sri Lankan
Undergraduates. This study used three unsupervised machine learning
techniques, namely Kmeans, Agglomerative clustering, and DBSCAN
clustering for the detection of patterns or relationships among datasets. To
find the accuracy of the algorithms the Silhouette value, Davies Boulding
value, and the Calinski-Harabasz value were used. Among those algorithms,
the best accuracy is shown in the K-means algorithm. Hence, this algorithm
has been selected to predict the pervasiveness of Obsessive-Compulsive
Disorder (OCD) as low, moderate, or high among Sri Lankan
Undergraduates.
