Unsupervised Data Mining and Effect of Fast Rehabilitation Nursing Intervention in Fracture Surgery

Abstract

At present, the most commonly used surgical treatment for fractures caused by external force injury is clinical, and unsupervised data mining is more advantageous in the face of the unknown format of perioperative network data. Therefore, this research aims to explore the application effect of unsupervised data mining in the concept of rapid rehabilitation nursing intervention after fracture surgery. 80 patients who underwent fracture surgery in the Department of Orthopedics of XXX Hospital were determined as the subjects, who were rolled into a research group (group R, 40 cases) and a control group (group C, 40 cases) by drawing lots. An unsupervised data mining algorithm based on unsupervised data mining for support vector machines (VDMSVMs) was proposed and applied to two groups of patients undergoing perioperative fracture surgery with the rapid rehabilitation nursing intervention and basic routine nursing. The results showed that the number of important features selected by the VDMSVM algorithm (5) was obviously more than that of the compressed edge fragment sampling (CEFS) algorithm (1) and the multicorrelation forward searching (MCFS) algorithm (2) ( $P<0.05$ ). The number of noise features screened by the VDMSVM algorithm (3) was much less in contrast to that of the CEFS algorithm and the MCFS algorithm, which was 8 and 10, respectively (P < 0.05). The Visual Analogue Scale (VAS) scores of the fracture site at the 4th, 8th, 12th, and 16th hour after surgery in group R were all lower than the scores in group C ( $P<0.05$ ). The length of hospital stay (LoHS) in group R was greatly shorter than that in group C ( $P<0.05$ ). After different nursing methods, the World Health Organization Quality of Life (WHOQOL-BREF) score of patients in group R (89.64 points) was greatly higher than the score in group C (61.45 points) ( $P<0.05$ ). The nursing satisfaction score of group R was 92.35 ± 3.65 points, and that in group C was 2.14 ± 1.25 points, respectively ( $P<0.05$ ). The test results verified the effectiveness of the feature selection of the VDMSVM algorithm. The rapid rehabilitation nursing intervention was conductive to reducing the postoperative pain of fracture patients, shortening the LoHS of patients, improving the quality of life (QOL) of fracture surgery patients, and increasing the patient’s satisfaction with nursing.