Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial
| Autor: | Mauro Castelli, Marcello Imbriani, Paola Baiardi, Marta Gianotti, Alfredo Raglio, Luca Manzoni, Sara Manzoni, Francisco J. Vico, Chiara Imbriani, Leonardo Vanneschi |
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| Přispěvatelé: | Raglio, Alfredo, Imbriani, Marcello, Imbriani, Chiara, Baiardi, Paola, Manzoni, Sara, Gianotti, Marta, Castelli, Mauro, Vanneschi, Leonardo, Vico, Francisco, Manzoni, Luca |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Music therapy Adolescent Visual analogue scale Decision tree Health Informatics Musical Music listening Machine learning computer.software_genre behavioral disciplines and activities 030218 nuclear medicine & medical imaging law.invention Machine Learning 03 medical and health sciences Therapeutic predictivity 0302 clinical medicine Randomized controlled trial law Humans Decision tree methods Machine learning techniques Medicine Therapeutic music listening Active listening Decision tree method Child Music Therapy Aged Relaxation (psychology) business.industry Infant Middle Aged Machine learning technique humanities Computer Science Applications Child Preschool Female Artificial intelligence Psychology business human activities computer 030217 neurology & neurosurgery Software |
| Popis: | Background The literature shows the effectiveness of music listening, but which factors and what types of music produce therapeutic effects, as well as how music therapists can select music, remain unclear. Here, we present a study to establish the main predictive factors of music listening's relaxation effects using machine learning methods. Methods Three hundred and twenty healthy participants were evenly distributed by age, education level, presence of musical training, and sex. Each of them listened to music for nine minutes (either to their preferred music or to algorithmically generated music). Relaxation levels were recorded using a visual analogue scale (VAS) before and after the listening experience. The participants were then divided into three classes: increase, decrease, or no change in relaxation. A decision tree was generated to predict the effect of music listening on relaxation. Results A decision tree with an overall accuracy of 0.79 was produced. An analysis of the structure of the decision tree yielded some inferences as to the most important factors in predicting the effect of music listening, particularly the initial relaxation level, the combination of education and musical training, age, and music listening frequency. Conclusions The resulting decision tree and analysis of this interpretable model makes it possible to find predictive factors that influence therapeutic music listening outcomes. The strong subjectivity of therapeutic music listening suggests the use of machine learning techniques as an important and innovative approach to supporting music therapy practice. |
| Databáze: | OpenAIRE |
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