Effective prediction on music therapy using hybrid SVM-ANN approach

Abstract

In this world, people are moving with lightning speed. Stress has become a usual thing we experience in our day to day routine. Some factors like work tension, emotional obstacles, brutality, etc lead to stress. Many health issues like headaches, heart problems, depression, etc and psychological issues arise in human beings due to stress. Music therapy gives qualitative results in balancing the physical and psychological issues. Music therapy is an expressive type of art therapy. There are many beneficial effects achieved through music therapy like relaxation, maintain blood pressure level, cure on medical disorders, stability in mood, and improve memory and sleep. Here we aimed to establish the main predictive factors of music listening’s relaxation and the prediction of music for music therapy using various machine learning algorithms such as Decision tree, Random Forest, Artificial Neural Network (ANN), Support Vector Machine (SVM) and hybrid of SVM ANN algorithm. The accuracy of these different methods is critically examined with the help of the accuracy performance metric. Various factors like age, gender, education level, music choice, visual analog scale score before and after listening to music for both individual and therapist suggestions on music are considered for prediction. Our study revealed that SVM-ANN hybrid classifier performance is much better than other machine learning algorithms.

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