scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

scholarly journals Application of three statistical models for predicting the risk of diabetes

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Siyu Liu ◽  
Yue Gao ◽  
Yuhang Shen ◽  
Min Zhang ◽  
Jingjing Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Logistic Regression ◽  
Decision Tree ◽  
Regression Model ◽  
Neural Network Model ◽  
Logistic Regression Model ◽  
Tree Model

Abstract Background At present, the proportion of undiagnosed diabetes in Chinese adults is as high as 15.5%. People with diabetes who are not treated and controlled in time may have various complications, such as cardiovascular and cerebrovascular diseases and diabetic foot disorders, which not only seriously affect the quality of life of people with diabetes but also impose a heavy burden on families and society. Therefore, prevention and control of type 2 diabetes is of great significance. Methods We constructed a logistic regression model, a neural network model and a decision tree model to analyse the risk factors for type 2 diabetes and then compared the prediction accuracy of the different models by calculating the area under the relative operating characteristic (ROC) curve and back-inputting the data into the model. Results The prevalence of type 2 diabetes in 4177 subjects who were not diagnosed with type 2 diabetes was 9.31%. The most influential factors associated with type 2 diabetes were triglyceride (TG) ≥ 1.17 mmol/L (odds ratio (OR) =2.233), age ≥ 70 years (OR = 1.734), hypertension (OR = 1.703), alcohol consumption (OR = 1.674), and total cholesterol≥5.2 mmol/L (TC) (OR = 1.463). The prediction accuracies of the three prediction models were 90.8, 91.2, and 90.7%, respectively, and the areas under curve (AUCs) were 0.711, 0.780, and 0.698, respectively. The differences in the AUCs after back propagation (BP) of the neural network model, logistic regression model and decision tree model were statistically significant (P < 0.05). Conclusion BP neural networks have a higher predictive power for identifying the associated risk factors of type 2 diabetes than the other two models, but it is necessary to select a suitable model for specific situations.

scholarly journals Associated Factors with the Mortality Rate in Patients with COVID-19 - Decision Trees Vs. Logistic Regression

2021 ◽  
Vol 10 (44) ◽  
pp. 3736-3741
Author(s):  
Soraya Siabani ◽  
Leila Solouki ◽  
Mehdi Moradinazar ◽  
Farid Najafi ◽  
Ebrahim Shakiba
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Body Temperature ◽  
Decision Tree ◽  
Regression Model ◽  
Roc Curve ◽  
Logistic Regression Model ◽  
Decision Tree Model ◽  
Tree Model

BACKGROUND Given the global burden of COVID-19 mortality, this study intended to determine the factors affecting mortality in patients with COVID-19 using decision tree analysis and logistic regression model in Kermanshah province, 2020. METHODS This cross-sectional study was conducted on 7799 patients with COVID-19 admitted to the hospitals of Kermanshah province. Data gathered from February 18 to July 9, 2020, were obtained from the vice-chancellor for the health of Kermanshah University of Medical Sciences. The performance of the models was compared according to the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. RESULTS According to the decision tree model, the most important risk factors for death due to COVID-19 were age, body temperature, admission to intensive care unit (ICU), prior hospital visit within the last 14 days, and cardiovascular disease. Also, the multivariate logistic regression model showed that the variables of age [OR = 4.47, 95 % CI: (3.16 -6.32)], shortness of breath [OR = 1.42, 95 % CI: (1.0-2.01)], ICU admission [OR = 3.75, 95 % CI: (2.47-5.68)], abnormal chest X-ray [OR = 1.93, 95 % CI: (1.06-3.41)], liver disease [OR = 5.05, 95 % CI (1.020-25.2)], body temperature [OR = 4.93, 95 % CI: (2.17-6.25)], and cardiovascular disease [OR = 2.15, 95 % CI: (1.27-3.06)] were significantly associated with the higher mortality of patients with COVID-19. The area under the ROC curve for the decision tree model and logistic regression was 0.77 and 0.75, respectively. CONCLUSIONS Identifying risk factors for mortality in patients with COVID-19 can provide more effective interventions in the early stages of treatment and improve the medical approaches provided by the medical staff. KEY WORDS COVID-19, Decision Tree, Logistic Regression, Mortality, Risk Factor

Statistical Process Control for Monitoring Standardized Mortality Ratios of a Classification Tree Model

2012 ◽  
Vol 51 (04) ◽  
pp. 353-358 ◽  
Author(s):  
S. Eslami ◽  
N. de Keizer ◽  
E. de Jonge ◽  
S. E. de Rooij ◽  
A. Abu-Hanna ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Brier Score ◽  
Tree Model ◽  
Care Assessment ◽  
Over Time

SummaryObjectives: The ratio of observed to expected mortality (standardized mortality ratio, SMR), is a key indicator of quality of care. We use PreControl Charts to investigate SMR behavior over time of an existing tree-model for predicting mortality in intensive care units (ICUs) and its implications for hospital ranking. We compare the results to those of a logistic regression model.Methods: We calculated SMRs of 30 equally-sized consecutive subsets from a total of 12,143 ICU patients aged 80 years or older and plotted them on a PreControl Chart. We calculated individual hospital SMRs in 2009, with and without repeated recalibration of the models on earlier data.Results: The overall SMR of the tree-model was stable over time, in contrast to logistic regression. Both models were stable after repeated recalibration. The overall SMR of the tree on the whole validation set was statistically significantly different (SMR 1.00 ± 0.012 vs. 0.94 ± 0.01) and worse in performance than the logistic regression model (AUC 0.76 ± 0.005 vs. 0.79 ± 0.004; Brier score 0.17 ± 0.012 vs. 0.16 ± 0.010). The individual SMRs’ range in 2009 was 0.53–1.31 for the tree and 0.64–1.27 for logistic regression. The proportion of individual hospitals with SMR >1, hinting at poor quality of care, reduced from 38% to 29% after recalibration for the tree, and increased from 15% to 35% for logistic regression.Conclusions: Although the tree-model has seemingly a longer shelf life than the logistic regression model, its SMR may be less useful for quality of care assessment as it insufficiently responds to changes in the population over time.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

scholarly journals A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
T Heseltine ◽  
SW Murray ◽  
RL Jones ◽  
M Fisher ◽  
B Ruzsics
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Coronary Artery Calcium ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Multinomial Logistic Regression ◽  
Cvd Risk ◽  
Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= &lt;0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= &lt;0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score &gt;400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score &gt;400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

scholarly journals Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anping Guo ◽  
Jin Lu ◽  
Haizhu Tan ◽  
Zejian Kuang ◽  
Ying Luo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Liver Disease ◽  
Length Of Stay ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospital Length ◽  
Clinical Treatment ◽  
Hospital Length Of Stay ◽  
Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

Relationship between childhood obesity and socioeconomic status among primary school children in Costa Rica

2021 ◽  
pp. 1-24
Author(s):  
Tatiana Gamboa-Gamboa ◽  
Romain Fantin ◽  
Jeancarlo Cordoba ◽  
Ivannia Caravaca ◽  
Ingrid Gómez-Duarte
Keyword(s):  
Socioeconomic Status ◽  
Logistic Regression ◽  
Childhood Obesity ◽  
Costa Rica ◽  
Regression Model ◽  
Primary Schools ◽  
Logistic Regression Model ◽  
Mixed Effects ◽  
Overweight And Obesity ◽  
Public And Private

Abstract Objective: This article analyzes the relationship between socioeconomic status and the prevalence of overweight and obesity in the primary school population in Costa Rica. Design: A National School Weight/Height Census was disseminated across Costa Rica in 2016. The percentage of children who were overweight or obese was calculated by sex, age, and socioeconomic indicators (type of institution: private, public, mix; type of geographic location: rural, urban; and the level of development of the district of residence: quartiles). A mixed effects multinomial logistic regression model and mixed effects logistic regression model were used to analyze the association between the prevalence of being overweight or obese and district socioeconomic status. Setting: The survey was carried out in public and private primary schools across Costa Rica in 2016. Participants: 347,366 students from 6 to 12 years old, enrolled in public and private primary schools. Results: The prevalence of overweight and obesity among children was 34.0%. Children in private schools were more likely to be overweight or obese than students in public schools (OR=1.10 [1.07, 1.13]). Additionally, children were less likely to be overweight or obese if attending a school in a district of the lowest socioeconomic quartile compared to the highest socioeconomic quartile (OR=0.79 [0.75, 0.83]), and in a rural area compared to the urban area (OR=0.92 [0.87, 0.97]). Conclusions: Childhood obesity in Costa Rica continues to be a public health problem. Prevalence of overweight and obesity in children was associated with indicators of higher socioeconomic status.

scholarly journals 1244. Evaluation of Intraoperative Topical Vancomycin and the Incidence of Acute Kidney Injury

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S448-S448
Author(s):  
Alison L Blackman ◽  
Sabeen Ali ◽  
Xin Gao ◽  
Rosina Mesumbe ◽  
Carly Cheng ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Serum Creatinine ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Kidney Injury ◽  
Topical Formulation ◽  
Multivariable Logistic Regression Model ◽  
Multivariable Logistic Regression

Abstract Background The use of intraoperative topical vancomycin (VAN) is a strategy aimed to prevent surgical site infections (SSI). Although there is evidence to support its efficacy in SSI prevention following orthopedic spine surgeries, data describing its safety, specifically acute kidney injury (AKI) risk, is limited. The purpose of this study was to determine the AKI incidence associated with intraoperative topical VAN. Methods This is a retrospective cohort study reviewing patient encounters where intraoperative topical VAN was administered from February 2018 to July 2018. All adult patients ( ≥18 years) that received topical VAN in the form of powder, beads, rods, paste, cement spacers, or unspecified topical routes were included. Patient encounters were excluded for AKI or renal replacement therapy (RRT) at baseline, ≤ 2 serum creatinine values drawn after surgery, and/or if irrigation was the only topical formulation given. The primary outcome was the percentage of patients who developed AKI after intraoperative topical VAN administration. AKI was defined as an increase in serum creatinine (SCr) ≥50% from baseline, an increase in SCr >0.5 from baseline, or0 if RRT was initiated after topical VAN was given. Secondary outcomes included analysis of AKI risk factors and SSI incidence. AKI risk factors were analyzed using a multivariable logistic regression model. Results A total of 589 patient encounters met study criteria. VAN powder was the most common formulation (40.9%), followed by unspecified topical routes (30.7%) and beads (9.9%%). Nonspinal orthopedic surgeries were the most common procedure performed 46.7%. The incidence of AKI was 8.7%. In a multivariable logistic regression model, AKI was associated with concomitant systemic VAN (OR 3.39, [3.39–6.22]) and total topical VAN dose. Each doubling of the topical dose was associated with increased odds of developing AKI (OR = 1.42, [1.08–1.86]). The incidence of SSI was 5.3%. Conclusion AKI rates associated with intraoperative topical VAN are comparable to that of systemic VAN. Total topical vancomycin dose and concomitant systemic VAN was associated with an increased AKI risk. Additional analysis is warranted to compare these patients to a similar population that did not receive topical VAN. Disclosures All authors: No reported disclosures.

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