Comparison of autoregressive integrated moving average model and deep learning model in prediction and analysis of liposuction operation data
10.3760/cma.j.cn114453-20201201-00603
- VernacularTitle:差分整合移动平均自回归模型与深度学习模型在吸脂操作数据预测分析中的应用比较
- Author:
Zhibin SUN
1
;
Gang ZHOU
;
Yuneng WANG
;
Sijie CHEN
;
Yu WANG
;
Facheng LI
;
Haiyue JIANG
Author Information
1. 北京航空航天大学生物医学工程学院 100191
- Keywords:
Lipectomy;
Forecasting;
Artificial intelligence;
Machine learning
- From:
Chinese Journal of Plastic Surgery
2021;37(8):970-976
- CountryChina
- Language:Chinese
-
Abstract:
Objective:This study aims to compare the application value of Autoregressive Integrated Moving Average model (ARIMA ) and deep learning model inprediction and analysis of liposuction operation data.Methods:The patients who met inclusion criteria and underwent liposuction surgery in the Plastic Surgery Hospital of Chinese Academy of Medical Sciences from January 2019 to September 2019 were enrolled in this study. For each patient, 250~400 s operation data including kinematics and mechanical data were collected by a senior plastic surgeon, usingthe liposuction operation recording system which consists of optical tracking and force sensing equipment. After pretreatment, the collected data were divided into one liposuction reciprocating cycle as one set of data. ARIMA model and deep learning model were used to analyze the collected datafor establishing prediction models of liposuction operation. Using Matlab2017, 30 couplesofliposuction data setwereextractedby simple random sampling, andthe DTW valueofeachcoupleofdatasetswascalculated as test standard. Then, the DTW values between 30 sets of predicted data and actual data based on the ARIMA model and the deep learning model were calculated respectively and compared with the test standard to verify the prediction result of the two models. Matlab2017 was used for statistical analysis. Independent sample t-test was used to compare the two groups, and P<0. 05 indicates that the difference is statistically significant.Results:18 patients were enrolled. All patients were females at 23-49 years old, with the mean age of 36. 6 years old. Liposuction was performed in the abdomen, thighs, and waist. A total of 16 800 sets of liposuction cycle data were obtained. The mean DTW value of test standard was 0. 048±0. 028. The meanDTW value between the ARIMA model predicted data and the actual data was 0. 660±0. 577, which was statistically significant compared with the test standard ( P< 0. 05) . The meanDTW value between the deep learning model predicted data and the actual data was 0. 052±0. 030, which was no significant difference compared with the test standard ( P> 0. 05 ). Conclusions:Compared with ARIMA model, deep learning model can predict liposuction operation data more accurately, and has better adaptability and real-time performance.
