# The Effects of Synthesizing Music Using AI for Preoperative Management of Patients' Anxiety
## 4 Discussion  
In this study, to synthesize music using AI for preoperative management of patients' anxiety, a stepwise approach was applied to improve quality using SDLC. Nurses working at the operating room entrance were asked to assess their preference using a GEMS-9 evaluation and an FGI for selecting the music to be used for machine learning. The analysis results were included in the final database for machine learning based on the findings. As a result, a raw MIDI file consisting of random notes without any particular regularity was obtained, but better results can be achieved with the assistance of music experts. As a result of the existing system analysis, despite various research results suggesting that preoperative music has a significant effect on patients' anxiety levels [8], in this study, the nurses stated that music was not actively used in the medical setting. This is due to the tendency of prioritizing performing aseptic methods and quick treatment in the operating room. Consequently, it is thought that the use of music has relatively little clinical significance and is regarded as less important [22; 23]. However, before anesthesia is administered to the patient, who is feeling extremely anxious about the surgery, feeling mentally stable and clam is important for the successful treatment outcome as well as the medical surgical procedure. Therefore, the importance of playing appropriate music that is developed according to the software development life cycle model can be a new effective approach. Figure 3: (**a**) Produced sample sound source; (**b**) edited sample sound source [[https://youtu.be/OOszeBCE7](https://youtu.be/OOszeBCE7) Xg]([https://youtu.be/OOszeBCE7](https://youtu.be/OOszeBCE7) Xg) (accessed on 7 August 2022). The Geneva Emotional Music Scales were derived from confirmatory factor analyses of ratings of emotions evoked by various genres of music. According to the results of the preference analysis, each song has different characteristics. As a result of GEMS-9, bossa nova, jazz, and classical piano were selected as suitable genres. Our results that bossa nova music is related to relaxation are similar to those of previous studies [24,25]. Using the Geneva Emotional Music Scales results can provide evidence-based results to artificially synthesize music suitable for patients in the hospital setting. The sound source obtained through the deep learning process in this study was not at a level that could be directly applied to the clinical field. This is due to the small number of scale sources used in deep learning. For practical use, conversion of the MIDI file to an original instrument using another music editing program is needed. Conversion of the MIDI file to an original song tempo, and performance with an accompaniment or guitar in addition to the main melody are recommended. Most existing studies related to surgery-related anxiety interventions selected the music preferred by the patient or provided music arbitrarily chosen by a relevant practitioner [26,27]. However, the operating room's waiting area was occupied with tasks such as preparations for surgery, so selecting and providing personalized music was laborious. When the same music was offered to all patients waiting, it was difficult to find previous studies that analyzed what criteria should be selected and applied. Therefore, this study will support nurses in selecting and providing appropriate music in the operating room's waiting area and help reduce patients' anxiety before surgery. Although this study focused only on the anxiety of patients, considering that music is heard by everyone in the operating room, it is necessary to consider the communication of medical staff, as was considered in other studies on music preference [28,29]. It is crucial to directly observe the results of this study as it is an indirect research study through operating room nurses rather than a study on actual patients in the operating room's waiting area.