Applying Particle Swarm Optimization-Base Decision Tree Classifier for Mental Illnesses

Abstract

Background: Data mining techniques such as clustering and classification are used to explore patient's data and extract a predictive model. Medical data set are often classified by a large number of irrelevant disease measurements(features). Feature selection is one of the most common tasks which reduces the computational cost by removing insignificant features.

Method: This paper presents a graph-based Louvain algorithm for mental illness dataset clustering and a particle swarm optimization combined with a decision tree as the classifier to select the small number of an informative feature from the thousands of features were collected from health centers consist of 1060 people in two groups of 550 patients and 510 healthy.

Result: The results show that "aggression" Finding the greatest impact on the diagnosis of mental disorders has been observed in the number of 65. After that, the features such as "prisoner in the family" and "hard labor" with 63 observations had a greater impact on the disease also the third ranking "illiterate" and "elation and euphoria" had 61 and 58 observations.

Conclusions: The classification accuracy shows that the proposed method is capable of producing good results with fewer features than the original datasets.

Keywords: Mental illness, Graph clustering, Particle swarm optimization, ID3

DOI: 10.7176/JIEA/9-7-03

Publication date: December 31^st 2019