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

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

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Application of monoexponential, biexponential, and stretched-exponential models of diffusion-weighted magnetic resonance imaging in the differential diagnosis of metastases and myeloma in the spine-Univariate and multivariate analysis of related parameters

British Journal of Radiology ◽

10.1259/bjr.20190891 ◽

2020 ◽

Vol 93 (1112) ◽

pp. 20190891

Author(s):

Xiaoying Xing ◽

Jiahui Zhang ◽

Yongye Chen ◽

Qiang Zhao ◽

Ning Lang ◽

...

Keyword(s):

Logistic Regression ◽

Differential Diagnosis ◽

Diffusion Coefficient ◽

Decision Tree ◽

Regression Model ◽

Logistic Regression Model ◽

Decision Tree Model ◽

Tree Model ◽

Stretched Exponential ◽

Exponential Models

Objective: To explore the value of related parameters in monoexponential, biexponential, and stretched-exponential models of diffusion-weighted imaging (DWI) in differentiating metastases and myeloma in the spine. Methods: 53 metastases and 16 myeloma patients underwent MRI with 10 b-values (0–1500 s/mm2). Parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), the distribution diffusion coefficient (DDC), and intravoxel water diffusion heterogeneity (α) from DWI were calculated. The independent sample t test and the Mann–Whiney U test were used to compare the statistical difference of the parameter values between the two. Receiver operating characteristics (ROC) curve analysis was used to identify the diagnostic efficacy. Then substituted each parameter into the decision tree model and logistic regression model, identified meaningful parameters, and evaluated their joint diagnostic performance. Results: The ADC, D, and α values of metastases were higher than those of myeloma, whereas the D* value was lower than that of myeloma, and the difference was significant (p < 0.05); the area under the ROC curve for the above parameters was 0.661, 0.710, 0.781, and 0.743, respectively. There was no significant difference in the f and DDC values (p > 0.05). D and α were found to conform to the decision tree model, and the accuracy of model diagnosis was 84.1%. ADC and α were found to conform to the logistic regression model, and the accuracy was 87.0%. Conclusion: The 3 models of DWI have certain values indifferentiating metastases and myeloma in spine, and the diagnostic performance of ADC, D, α and D*was better. Combining ADC with α may markedly aid in the differential diagnosis of the two. Advances in knowledge: Monoexponential, biexponential, and stretched-exponential models can offer additional information in the differential diagnosis of metastases and myeloma in the spine. Decision tree model and logistic regression model are effective methods to help further distinguish the two.

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Application of three statistical models for predicting the risk of diabetes

BMC Endocrine Disorders ◽

10.1186/s12902-019-0456-2 ◽

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.

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

10.21203/rs.3.rs-390569/v1 ◽

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.

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Research on Driver Behavior in Yellow Interval at Signalized Intersections

Mathematical Problems in Engineering ◽

10.1155/2014/518782 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Zhaosheng Yang ◽

Xiujuan Tian ◽

Wei Wang ◽

Xiyang Zhou ◽

Hongmei Liang

Keyword(s):

Logistic Regression ◽

Decision Tree ◽

Regression Model ◽

Logistic Regression Model ◽

Driver Behavior ◽

Signalized Intersections ◽

Fuzzy Decision ◽

Fuzzy Decision Tree ◽

Tree Model ◽

Dilemma Zone

Vehicles are often caught in dilemma zone when they approach signalized intersections in yellow interval. The existence of dilemma zone which is significantly influenced by driver behavior seriously affects the efficiency and safety of intersections. This paper proposes the driver behavior models in yellow interval by logistic regression and fuzzy decision tree modeling, respectively, based on camera image data. Vehicle’s speed and distance to stop line are considered in logistic regression model, which also brings in a dummy variable to describe installation of countdown timer display. Fuzzy decision tree model is generated by FID3 algorithm whose heuristic information is fuzzy information entropy based on membership functions. This paper concludes that fuzzy decision tree is more accurate to describe driver behavior at signalized intersection than logistic regression model.

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Based on a Decision Tree Model for Exploring the Risk Factors of Smartphone Addiction Among Children and Adolescents in China During the COVID-19 Pandemic

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.652356 ◽

2021 ◽

Vol 12 ◽

Author(s):

Li Duan ◽

Juan He ◽

Min Li ◽

Jiali Dai ◽

Yurong Zhou ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Decision Tree ◽

Children And Adolescents ◽

Predictive Value ◽

Mainland China ◽

Decision Tree Model ◽

Smartphone Addiction ◽

Physical And Mental Health ◽

Tree Model

Background: Smartphone addiction has emerged as a major concern among children and adolescents over the past few decades and may be heightened by the outbreak of COVID-19, posing a threat to their physical and mental health. Then we aimed to develop a decision tree model as a screening tool for unrecognized smartphone addiction by conducting large sample investigation in mainland China.Methods: The data from cross-sectional investigation of smartphone addiction among children and adolescents in mainland China (n = 3,615) was used to build models of smartphone addiction by employing logistic regression, visualized nomogram, and decision tree analysis.Results: Smartphone addiction was found in 849 (23.5%) of the 3,615 respondents. According to the results of logistic regression, nomogram, and decision tree analyses, Internet addiction, hours spend on smartphone during the epidemic, levels of clinical anxiety symptoms, fear of physical injury, and sex were used in predictive model of smartphone addiction among children and adolescents. The C-index of the final adjusted model of logistic regression was 0.804. The classification accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC area of decision tree for detecting smartphone addiction were 87.3, 71.4, 92.1, 73.5, 91.4, and 0.884, respectively.Conclusions: It was found that the incidence of smartphone addiction among children and adolescents is significant during the epidemic. The decision tree model can be used to screen smartphone addiction among them. Findings of the five risk factors will help researchers and parents assess the risk of smartphone addiction quickly and easily.

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

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jeaa356.234 ◽

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= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <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 >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 >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.

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

Scientific Reports ◽

10.1038/s41598-021-86853-4 ◽

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.

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Prediction of Student Dropout in Malaysian’s Private Higher Education Institute using Data Mining Application

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i3.1219 ◽

2021 ◽

Vol 12 (3) ◽

pp. 2326-2334

Author(s):

Nurhana Roslan Et.al

Keyword(s):

Logistic Regression ◽

Decision Tree ◽

Regression Model ◽

Logistic Regression Model ◽

Performance Measure ◽

Data Partitioning ◽

Learning Performance ◽

Misclassification Rate ◽

Student Dropout ◽

The University

Student dropout issue is a major concern among the academics and management of the university. The higher rate of student dropout impacted the university reputation such as reducing student enrollment, affecting the revenue of the university, financial losses for the country, and increase the existence of a social problem among the students. In this study, 2 popular classifiers were utilized to predict the student dropout namely decision tree and logistic regression model respectively. Several sets of experimental setting were employed which include three set of data partitioning - along with different types of decision tree and regression model. As for the logistic regression model, different data imputation and transformation method was tested to ensure that the model built is valid. A total of 7706 student data extracted from one of the private universities in Malaysia database (between year 2018-2019) to assess the capability of the classifier. The classifier performance is evaluated using machine learning performance measure of accuracy and misclassification rate. The result indicates that, decision tree - chi-square (2 branches) achieved slightly better classification performance of 89.49% on 80/20 data partitioning. The chosen model also identified the most important variable for accurate prediction of student dropout. Application of this model has the potential to accurately predict at risk student and to reduce student dropout rates.

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

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.1107 ◽

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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Surgical Site Infection in a Tertiary Neonatal Surgery Centre

European Journal of Pediatric Surgery ◽

10.1055/s-0038-1636916 ◽

2018 ◽

Vol 29 (03) ◽

pp. 260-265 ◽

Cited By ~ 6

Author(s):

Adiam Woldemicael ◽

Sarah Bradley ◽

Caroline Pardy ◽

Justin Richards ◽

Paolo Trerotoli ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Model ◽

Surgical Site Infection ◽

Hospital Stay ◽

Logistic Regression Model ◽

Length Of Hospital Stay ◽

Neonatal Surgery ◽

Site Infection ◽

Increased Risk

Introduction Surgical site infection (SSI) is a key performance indicator to assess the quality of surgical care. Incidence and risk factors for SSI in neonatal surgery are lacking in the literature. Aim To define the incidence of SSI and possible risk factors in a tertiary neonatal surgery centre. Materials and Methods This is a prospective cohort study of all the neonates who underwent abdominal and thoracic surgery between March 2012 and October 2016. The variables analyzed were gender, gestational age, birth weight, age at surgery, preoperative stay in neonatal intensive care unit, type of surgery, length of stay, and microorganisms isolated from the wounds. Statistical analysis was done with chi-square, Student's t- or Mann–Whitney U-tests. A logistic regression model was used to evaluate determinants of risk for SSI; variables were analyzed both with univariate and multivariate models. For the length of hospital stay, a logistic regression model was performed with independent variables. Results A total of 244 neonates underwent 319 surgical procedures. The overall incidence of SSIs was 43/319 (13.5%). The only statistical differences between neonates with and without SSI were preoperative stay (<4 days vs. ≥4 days, p < 0.01) and length of hospital stay (<30 days vs. ≥30 days, p < 0.01). A pre-operative stay longer than 4 days was associated with almost three times increased risk of SSI (odds ratio [OR] 2.96, 95% confidence interval [CI] 1.05–8.34, p = 0.0407). Gastrointestinal procedures were associated with more than ten times the risk of SSI compared with other procedures (OR 10.17, 95% CI 3.82–27.10, p < 0.0001). Gastroschisis closure and necrotizing enterocolitis (NEC) laparotomies had the highest incidence SSI (54% and 62%, respectively). The risk of longer length of hospital stay after SSI was more than three times higher (OR = 3.36, 95%CI 1.63–6.94, p = 0.001). Conclusion This is the first article benchmarking the incidence of SSI in neonatal surgery in the United Kingdom. A preoperative stay ≥4 days and gastrointestinal procedures were independent risk factors for SSI. More research is needed to develop strategies to reduce SSI in selected neonatal procedures.

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