Prediction of Mental Health Disorders using Radial Basis Function Network

Abstract

Mental illness has become a social issue and could become a cause of functional disability during routine life. In addition it could implicate several psycho physiological disorders. The latest neuroscience reveals that mental sickness is growing at epidemic rates around the world and there is a essential requirement to take care of initial psychological health problem to improve patient’s quality of life. Now a days Machine learning algorithms are well suited for analyzing medical data and diagnosing the medical problem This paper has analyzed the performance of   two machine learning techniques for predicting mental health disorders. For testing and training the machine learning algorithm a data set consisting of  hundred cases is collected and  fifteen attributes has been recognized for detecting  the mental health  problem from the document. It is evident from the result  that Radial Basis Function Network produced more accurate results than Spectral Clustering Classifier.