This paper discusses the risk factors related to gallbladder disease in Shanghai, improves the accuracy of risk prediction, and provides a theoretical basis for scientific diagnosis and universality of gallbladder disease.We selected 3 462 data of middle-aged and elderly health check-up patients in a general hospital in Shanghai,and divided into gallbladder disease group according to color doppler ultrasound diagnosis results. Single-factor analysis screened out 8 important risk factors, which were used as an analysis variable of multi-layer perceptron neural network and binary logistic regression to construct the prediction model of gallbladder disease. The prediction accuracy of the multi-layer perceptron neural network risk prediction model is 76%. The area under the receiver operating characteristic curve (AUC) is 0.82, the maximum Youden index is 0.44, the sensitivity is 79.51, and the specificity is 64.23. The prediction accuracy of the multi-layer perceptron neural network model is better than that of the binary logistic regression prediction model. The overall prediction accuracy of the binary logistic regression prediction model is 75.60%, the AUC is 0.81, the maximum Youden index is 0.42, the sensitivity is 74.48, and the specificity is 57.60. In the objective risk prediction of gallbladder disease in middle-aged and elderly people in Shanghai, the risk prediction model based on the multi-layer perceptron neural network has a better prediction performance than the binary logistic regression model, which provides a theoretical basis for preventive treatment and intervention.
YUAN Xiaoqi (袁筱祺), ZHU Lelan (朱乐兰), XU Qiongfan(徐琼凡), GAO Wei (高玮)
. Risk Prediction Model of Gallbladder Disease in Shanghai Middle-Aged and Elderly People Based on Neural Networks[J]. Journal of Shanghai Jiaotong University(Science), 2022
, 27(2)
: 153
-159
.
DOI: 10.1007/s12204-021-2386-1
[1] Working Group on Obesity in China. Guidelines for prevention and control of overweight and obesity in Chinese adults [J]. Acta Nutrimental Sinica, 2004,26(1): 1-4 (in Chinese).
[2] STINTON L M, SHAFFER E A. Epidemiology of gallbladder disease: Cholelithiasis and cancer [J]. Gut and Liver, 2012, 6(2): 172-187.
[3] JESSRI M, RASHIDKHANI B. Dietary patterns and risk of gallbladder disease: A hospital-based casecontrol study in adult women [J]. Journal of Health,Population, and Nutrition, 2015, 33(1): 39-49.
[4] LANDER E M, WERTHEIM B C, KOCH S M, et al. Vegetable protein intake is associated with lower gallbladder disease risk: Findings from the Women’s Health Initiative prospective cohort [J]. Preventive Medicine, 2016, 88: 20-26.
[5] SHABANZADEH D M, S?RENSEN L T,J?RGENSEN T. A prediction rule for risk stratification of incidentally discovered gallstones: Results from a large cohort study [J]. Gastroenterology, 2016,150(1): 156-167.
[6] FIGUEIREDO J C, HAIMAN C, PORCEL J, et al. Sex and ethnic/racial-specific risk factors for gallbladder disease [J]. BMC Gastroenterology, 2017, 17(1):1-12.
[7] XIE S S, XING Y R, YANG Z X. Analysis on epidemiological characteristics and risk factors of fatty liver complicating gallbladder diseases among healthy physical examination people [J]. Chongqing Medicine,2018, 47(1): 76-78 (in Chinese).
[8] KIM H S, CHO S K, KIM C S, et al. Big data and analysis of risk factors for gallbladder disease in the young generation of Korea [J]. PLoS One, 2019, 14(2): e0211480.
[9] RAMOS E, LLUIS N, LLADO L, et al. Prognostic value and risk stratification of residual disease in patients with incidental gallbladder cancer [J]. World Journal of Surgical Oncology, 2020, 18: 18.
[10] GUO Z Y, YAO Y F. Correlation analysis of epidemiological characteristics and risk factors of gallbladder diseases in elderly people during physical examination[J]. Shanxi Medical Journal, 2020, 49(16): 2095-2098 (in Chinese).
[11] LV F, WANG G, DING D, et al. Epidemiological survey to determine the prevalence of cholecystolithiasis in Uyghur, Kazakh, and Han Ethnic Groups in the Xinjiang Uyghur Autonomous Region of China: Crosssectional studies [J]. BMC Gastroenterology, 2021,21(1): 125.
[12] ZHANG T, ZHANG J G, ZHANG X L, et al.Element-based multi-layer perceptron neural network rock recognition [J]. Ground Water, 2016, 38(5): 107-109 (in Chinese).
[13] CHENG Z J. Research and application of large scale multi-layer perceptron neural network [D]. Changchun,China: Jilin University, 2016 (in Chinese).
[14] WU Y, ZHOU M J. Clinical features and influencing factors of cholecystolithiasis with fatty liver [J]. Chinese Journal of Clinical Gastroenterology, 2019, 31(2):100-104 (in Chinese).
[15] MA C, TONKS K T, CENTER J R, et al. Complex interplay among adiposity, insulin resistance and bone health [J]. Clinical Obesity, 2018, 8(2): 131-139.
[16] ZHOU B, SU X, SU D, et al. Dietary intake of manganese and the risk of the metabolic syndrome in a Chinese population [J]. The British Journal of Nutrition,2016, 116(5): 853-863.
[17] LIN W R, LIN D Y, TAI D I, et al. Prevalence of and risk factors for gallbladder polyps detected by ultrasonography among healthy Chinese: Analysis of 34669 cases [J]. Journal of Gastroenterology and Hepatology,2008, 23(6): 965-969.
