Exploring Therapeutic Indications and Clinical Applications of Music Therapy via Data Mining

This article aims to explore the therapeutic indications of music therapy and clinical application characteristics using data mining (DM) techniques. By systematically collecting and preprocessing relevant data, and selecting key features, a decision tree (DT) model is constructed to identify the therapeutic indications of music therapy. Following model training and validation, this study analyzes various conditions' responsiveness to music therapy, as well as the impact of music type, treatment duration, and patient age on therapeutic outcomes. The results show that anxiety, depression, chronic pain, post-stroke rehabilitation, and cardiovascular rehabilitation are recognized as the therapeutic indications of music therapy, with a high response rate. Classical music has the best effect in improving these symptoms, and the increase in treatment time helps improve the curative effect. In addition, the patient's age also influences the curative impact of music therapy. These findings verify the effectiveness of music therapy and lay a foundation for its application and promotion in the future.