Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.

BACKGROUND: This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs). METHODS: Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model. RESULTS: The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia. CONCLUSIONS: The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.

Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier.

BACKGROUND: Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM. METHODS: Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model. RESULTS: According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes. CONCLUSIONS: The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data.

BACKGROUND AND OBJECTIVE: Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. METHODS: We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. RESULTS: The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. CONCLUSIONS: This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.

Exploring influencing factors of chronic obstructive pulmonary disease based on elastic net and Bayesian network.

This study aimed to construct Bayesian networks (BNs) to analyze the network relationships between COPD and its influencing factors, and the strength of each factor's influence on COPD was reflected through network reasoning. Elastic Net and Max-Min Hill-Climbing (MMHC) algorithm were adopted to screen the variables on the surveillance data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. 10 variables finally entered the model after screening by Elastic Net. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients' cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network linkages between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.

Prevalence of Osteoporosis and Fracture in China: The China Osteoporosis Prevalence Study.

Importance: The aging of the population is associated with an increasing burden of fractures worldwide. However, the epidemiological features of fractures in mainland China are not well known. Objective: To assess the prevalence of and factors associated with osteoporosis, clinical fractures, and vertebral fractures in an adult population 40 years or older in mainland China. Design, Setting. and Participants: This cross-sectional study, the China Osteoporosis Prevalence Study, was conducted from December 2017 to August 2018. A random sample of individuals aged 20 years or older who represented urban and rural areas of China were enrolled, with a 99% participation rate. Main Outcomes and Measures: Weighted prevalence of osteoporosis, clinical fracture, and vertebral fracture by age, sex, and urban vs rural residence as determined by x-ray absorptiometry, questionnaire, and radiography. Results: A total of 20 416 participants were included in this study; 20 164 (98.8%; 11 443 women [56.7%]; mean [SD] age, 53 [13] years) had a qualified x-ray absorptiometry image and completed the questionnaire, and 8423 of 8800 (95.7%) had a qualified spine radiograph. The prevalence of osteoporosis among those aged 40 years or older was 5.0% (95% CI, 4.2%-5.8%) among men and 20.6% (95% CI, 19.3%-22.0%) among women. The prevalence of vertebral fracture was 10.5% (95% CI, 9.0%-12.0%) among men and 9.7% (95% CI, 8.2%-11.1%) among women. The prevalence of clinical fracture in the past 5 years was 4.1% (95% CI, 3.3%-4.9%) among men and 4.2% (95% CI, 3.6%-4.7%) among women. Among men and women, 0.3% (95% CI, 0.0%-0.7%) and 1.4% (95% CI, 0.8%-2.0%), respectively, with osteoporosis diagnosed on the basis of bone mineral density or with fracture were receiving antiosteoporosis treatment to prevent fracture. Conclusions and Relevance: In this cross-sectional study of an adult population in mainland China, the prevalence of osteoporosis and vertebral fracture were high and the prevalence of vertebral fracture and clinical fracture was similarly high in men and women. These findings suggest that current guidelines for screening and treatment of fractures among patients in China should focus equally on men and women and should emphasize the prevention of vertebral fractures.

Dietary Patterns Associated with Cognitive Function among the Older People in Underdeveloped Regions: Finding from the NCDFaC Study.

Although dietary patterns are crucial to cognitive function, associations of dietary patterns with cognitive function have not yet been fully understood. This cross-sectional study explored dietary patterns associated with cognitive function among the older adults in underdeveloped regions, using 1504 community-dwelling older adults aged 60 and over. Diet was assessed using a food frequency questionnaire and 24-h dietary recall. Factor analysis was used to extract dietary patterns. Global cognitive function was assessed using the Mini-Mental State Examination (MMSE). Two dietary patterns, a "mushroom, vegetable, and fruits" (MVF) pattern and a "meat and soybean products" (MS) pattern, were identified. The MVF pattern, characterized by high consumption of mushrooms, vegetables, and fruits was significantly positively associated with cognitive function (p < 0.05), with an odds ratio of (95% CIs) 0.60 (0.38, 0.94) for cognitive impairment and ß (95% CIs) 0.15 (0.02, 0.29) for -log (31-MMSE score). The MS pattern, characterized by high consumption of soybean products and meat, was also associated with better cognitive function, with an odds ratio of 0.47 (95% CIs 0.30, 0.74) for cognitive impairment and ß (95% CIs) 0.34 (0.21, 0.47) for -log (31-MMSE score). Our results suggested that both the MVF and MS patterns were positively associated with better cognitive function among older adults in underdeveloped regions.

Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors.

This study aimed to obtain the prevalence of hyperlipidemia and its related factors in Shanxi Province, China using multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs). A multi-stage stratified random sampling method was adopted to obtain samples among the general population aged 18 years or above. The prevalence of hyperlipidemia in Shanxi Province was 42.6%. Multivariate logistic regression analysis indicated that gender, age, region, occupation, vegetable intake level, physical activity, body mass index, central obesity, hypertension, and diabetes mellitus are associated with hyperlipidemia. BNs were used to find connections between those related factors and hyperlipidemia, which were established by a complex network structure. The results showed that BNs can not only be used to find out the correlative factors of hyperlipidemia but also to analyse how these factors affect hyperlipidemia and their interrelationships, which is consistent with practical theory, is superior to logistic regression and has better application prospects.

[Distribution and timing of antibody to SARS-CoV in SARS cases of transmission chain or non-transmission chain].

BACKGROUND: To find out the timing of serologic responses after illness onset and distribution of IgG antibody to SARS-CoV in SARS cases of transmission chain or non-transmission chain. METHODS: The IgG and IgM antibodies to SARS-CoV were tested by indirect ELISA in serum samples from 301 clinically diagnosed SARS cases. RESULTS: Totally 158 SARS cases were involved in 15 chains of transmission. The positive rates of SARS-CoV IgG in those chains were 85.70%-100.00% and the overall rate was 94.30% (149/158). The chain of transmission could spread to four generations, but the SARS cases were reduced with increase of generations. There was no significant difference among positive rates of SARS-CoV IgG for generations, Chi square=5.11, P greater than 0.05. The positive rate of SARS-CoV IgG in cases who were not in chain of transmission was 12.59%(18/143) which was statistically significantly different from that of cases in chain of transmission, Chi square=199.64, P less than 0.001. During days 0-7,8-14,15-21,22-30 after onset, the cumulated positive rate of SARS-CoV IgG was 16.67%, 40.00%, 70.00% and 93.10%, respectively, then was kept at the level above 90% and lasted for 217 days. The cumulated positive rate of SARS-CoV IgM during days 0-7 after onset was the same to that of IgG. During days 8-14, 55.17% of cases had seroconversion for IgM which reached a peak (86.96%) during days 21-30. Then the rate rapidly declined. CONCLUSION: More than 94% of cases with SARS could produce IgG antibody when they were infected by SARS-CoV. Detecting SARS-CoV IgG could provide a diagnostic evidence for case confirmation. SARS-CoV IgG appeared as early as 7 days after onset and reached the peak at about weeks 4. Then the high rate of antibody was maintained for more than 6 months.