Objective To analyze the prevalence of leukemia in China from 1990 to 2019, predict the incidence, morbidity and mortality of leukemia in China from 2020 to 2040, and provides reference for the formulation of leukemia-related prevention and treatment strategies in China. Methods Based on the 2019 Global Burden of Disease database, the incidence, morbidity and mortality data of leukemia in China from 1990 to 2019 were collected, and the rate of change and annual estimated percentage of change (EAPC) were used to describe the epidemic trend of the disease. The Autoregressive Moving Average (ARIMA) model was used to predict the prevalence of leukemia in China from 2020 to 2040. Results In 2019, the age-standardized incidence, age-standardized prevalence and age-standardized mortality rate of leukemia in China decreased by 17.62%, 10.97%, and 41.56%, respectively, compared with 1990, and an average annual decrease of 1.06%, 0.89%, and 2.05%, respectively (P<0.05). From 1990 to 2019, the reduction age-standardized incidence rate, age-standardized prevalence rate and age-standardized mortality rate in Chinese women (EAPC was 1.56%, 1.38%, and 2.62%, respectively) was higher than that of men (EAPC was 0.61%, 0.43%, and 1.59%, respectively). In 2019, the incidence and prevalence were highest in the age group under 5 years of age, and the mortality rate was the highest in the age group over 80 years old. The prediction results of ARIMA model showed that the age-standardized incidence rate and prevalence of leukemia in China showed an increasing trend from 2020 to 2040, while the age-standardized mortality rate showed a decreasing trend. It is estimated that by 2040, the age-standardized incidence rate, age-standardized prevalence rate, and age-standardized mortality rate of leukemia will be 14.06/100 000, 108.23/100 000, and 2.83/100 000. Conclusions From 1990 to 2019, the age-standardized incidence rate, age-standardized prevalence rate and age-standardized mortality rate of leukemia in China decreased year by year, but they were still at a high level. The prediction results show that the age-standardized incidence rate and age-standardized prevalence rate of leukemia in China will continue to increase from 2020 to 2040, and it is necessary to continue to strengthen the surveillance, prevention and control of leukemia in the future.
Objective To analyze the risk factors of type 2 diabetes mellitus and establish BP neural network model for screening of type 2 diabetes mellitus based on particle swarm optimization (PSO) algorithm. Methods Inpatients with type 2 diabetes mellitus in the Department of Endocrinology of the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between July 2021 and August 2022 were selected as the case group and healthy people in the Health Management Center of the Affiliated Hospital of Guangdong Medical University as the control group. Basic information and physical and laboratory examination indicators were collected for comparative analysis. PSO-BP neural network model, BP neural network model and logistic regression models were established using MATLAB R2021b software and the optimal screening model of type 2 diabetes mellitus was selected. Based on the optimal model, the mean impact value algorithm was used to screen the risk factors of type 2 diabetes mellitus. Results A total of 1 053 patients were included in the case group and 914 healthy peoples in the control group. Except for type of salt, family history of comorbidities, body mass index, total cholesterol, low density lipoprotein cholesterol and staple food intake (P>0.05), the other indexes showed significant differences between the two groups. The performance of the PSO-BP neural network model outperformed the BP neural network model and the logistic regression model. Based on PSO-BP neural network model, the mean impact value algorithm showed that the risk factors for type 2 diabetes mellitus were fasting blood glucose , heart rate, age , waist-arm ratio and marital status , and the protective factors for type 2 diabetes mellitus were high density lipoprotein cholestero, vegetable intake, residence, education level, fruit intake and meat intake. Conclusions There are many influencing factors of type 2 diabetes mellitus. Focus should be placed on high-risk groups and regular disease screening should be carried out to reduce the risk of type 2 diabetes. The screening model of PSO-BP neural network performs the best, and it can be extended to the early screening and diagnosis of other diseases in the future.