OPTIMIZING ACUPUNCTURE TREATMENT FOR PARKINSON’S DISEASE IN ATHLETES: A DATA MINING APPROACH TO ACUPOINT COMPATIBILITY

Abstract

Objective: This study leverages data mining techniques to optimize acupoint compatibility and enhance the effectiveness of acupuncture and moxibustion in treating Parkinson's Disease (PD) among athletes, a group increasingly experiencing degenerative neurological conditions as population ages. Methods: We employed data mining to analyze traditional acupuncture treatments and patterns used for PD, focusing on the systematic selection and combination of acupoints. The study involved a deep dive into the historical and theoretical basis of acupoint choices, including principles of selection, methods of acupoint pairing, and classification based on body parts and meridian pathways. Results: Our data-driven approach reviewed historical and current practices in acupuncture, identifying optimal patterns of meridian point allocations tailored for PD treatment. The introduction of data mining to determine meridian point patterns resulted in a newly constructed acupoint allocation model that significantly increased treatment satisfaction rates among athletes to 69%. Conclusion: The application of data mining in developing acupuncture treatment strategies for PD presents a promising avenue for enhancing therapeutic outcomes in sports medicine. By tailoring acupoint selection and pairing to the specific needs of athletes with PD, this approach not only addresses the unique challenges of treating degenerative diseases in this population but also improves overall treatment satisfaction. Future research should focus on longitudinal studies to validate these findings and explore their applicability in broader sports rehabilitation settings.