Abstract
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.
| Original language | English |
|---|---|
| Article number | 105160 |
| Journal | Computer Methods and Programs in Biomedicine |
| Volume | 185 |
| DOIs | |
| Publication status | Published - 1 Mar 2020 |
Fingerprint
Keywords
- Decision tree methods
- Machine learning techniques
- Medicine
- Therapeutic music listening
- Therapeutic predictivity
Cite this
}
Machine learning techniques to predict the effectiveness of music therapy : A randomized controlled trial. / Raglio, Alfredo; Imbriani, Marcello; Imbriani, Chiara; Baiardi, Paola; Manzoni, Sara; Gianotti, Marta; Castelli, Mauro; Vanneschi, Leonardo; Vico, Francisco; Manzoni, Luca.
In: Computer Methods and Programs in Biomedicine, Vol. 185, 105160, 01.03.2020.Research output: Contribution to journal › Article
TY - JOUR
T1 - Machine learning techniques to predict the effectiveness of music therapy
T2 - A randomized controlled trial
AU - Raglio, Alfredo
AU - Imbriani, Marcello
AU - Imbriani, Chiara
AU - Baiardi, Paola
AU - Manzoni, Sara
AU - Gianotti, Marta
AU - Castelli, Mauro
AU - Vanneschi, Leonardo
AU - Vico, Francisco
AU - Manzoni, Luca
N1 - Raglio, A., Imbriani, M., Imbriani, C., Baiardi, P., Manzoni, S., Gianotti, M., ... Manzoni, L. (2020). Machine learning techniques to predict the effectiveness of music therapy: A randomized controlled trial. Computer Methods and Programs in Biomedicine, 185, [105160]. https://doi.org/10.1016/j.cmpb.2019.105160
PY - 2020/3/1
Y1 - 2020/3/1
N2 - 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.
AB - 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.
KW - Decision tree methods
KW - Machine learning techniques
KW - Medicine
KW - Therapeutic music listening
KW - Therapeutic predictivity
UR - http://www.scopus.com/inward/record.url?scp=85074451949&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2019.105160
DO - 10.1016/j.cmpb.2019.105160
M3 - Article
VL - 185
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
SN - 0169-2607
M1 - 105160
ER -