Application and Deconstruction of Exercise Prescription Formulation Based on K-Means Algorithm in the Prevention and Treatment of Chronic Diseases

Abstract

Chronic diseases, also known as chronic noncommunicable diseases, have the characteristics of a long period of symptoms, complex and diverse causes, relatively large damage to human health, and a relatively wide impact on the overall safety of society. This study mainly discusses the application of exercise prescription formulation based on K-means algorithm in the prevention and treatment of chronic diseases. Aiming at different groups of people with different physical conditions in different environments, this study established a comprehensive exercise prescription library and feedback channels. By comparing and analyzing the effects of different exercise exercises, people can provide scientifically standardized and suitable exercise and fitness programs for people with chronic diseases. The feasibility of the K-means algorithm in chronic disease prediction is confirmed by experiments, and the experimental time of the improved algorithm and the traditional algorithm is compared, and the efficiency of the improved algorithm is confirmed. Aiming at the privacy, complexity, missing data values, and other issues of chronic disease medical examination data, we have carried out perfect data preprocessing research. After 12 weeks of exercise intervention, the vital capacity of the exercise group increased significantly (5.83%) ( $P<0.01$ ). The subendocardial myocardial viability rate increased by 9.74% ( $P<0.01$ ), and the radial artery reflected wave growth index and systolic blood pressure decreased slightly ( $P<0.05$ ). This indicates that the 12-week exercise training intervention program designed in this study can effectively enhance the cardiopulmonary function of the exercise group, thereby further improving the body’s aerobic capacity ( $P<0.05$ ). This study lays the foundation for the smooth development of chronic disease prevention and treatment.