scholarly journals 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

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.

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

scholarly journals Research on Driver Behavior in Yellow Interval at Signalized Intersections

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
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.

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 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.

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

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.

scholarly journals Decision Tree Analysis: A Retrospective Analysis of Postoperative Recurrence of Adhesions in Patients with Moderate-to-Severe Intrauterine

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Ru Zhu ◽  
Hua Duan ◽  
Sha Wang ◽  
Lu Gan ◽  
Qian Xu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Endometrial Thickness ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Decision Tree Model ◽  
Classification And Regression Tree ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Tree Model

Objective. To establish and validate a decision tree model to predict the recurrence of intrauterine adhesions (IUAs) in patients after separation of moderate-to-severe IUAs. Design. A retrospective study. Setting. A tertiary hysteroscopic center at a teaching hospital. Population. Patients were retrospectively selected who had undergone hysteroscopic adhesion separation surgery for treatment of moderate-to-severe IUAs. Interventions. Hysteroscopic adhesion separation surgery and second-look hysteroscopy 3 months later. Measurements and Main Results. Patients’ demographics, clinical indicators, and hysteroscopy data were collected from the electronic database of the hospital. The patients were randomly apportioned to either a training or testing set (332 and 142 patients, respectively). A decision tree model of adhesion recurrence was established with a classification and regression tree algorithm and validated with reference to a multivariate logistic regression model. The decision tree model was constructed based on the training set. The classification node variables were the risk factors for recurrence of IUAs: American Fertility Society score (root node variable), isolation barrier, endometrial thickness, tubal opening, uterine volume, and menstrual volume. The accuracies of the decision tree model and multivariate logistic regression analysis model were 75.35% and 76.06%, respectively, and areas under the receiver operating characteristic curve were 0.763 (95% CI 0.681–0.846) and 0.785 (95% CI 0.702–0.868). Conclusions. The decision tree model can readily predict the recurrence of IUAs and provides a new theoretical basis upon which clinicians can make appropriate clinical decisions.

scholarly journals Prediction of health care expenditure increase: how does pharmacotherapy contribute?

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Annika M. Jödicke ◽  
Urs Zellweger ◽  
Ivan T. Tomka ◽  
Thomas Neuer ◽  
Ivanka Curkovic ◽  
...  
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Decision Tree ◽  
Health Care Costs ◽  
Decision Tree Model ◽  
Tree Model ◽  
Physician Visits ◽  
Drug Classes ◽  
Swiss Health ◽  
Care Costs

Abstract Background Rising health care costs are a major public health issue. Thus, accurately predicting future costs and understanding which factors contribute to increases in health care expenditures are important. The objective of this project was to predict patients healthcare costs development in the subsequent year and to identify factors contributing to this prediction, with a particular focus on the role of pharmacotherapy. Methods We used 2014–2015 Swiss health insurance claims data on 373′264 adult patients to classify individuals’ changes in health care costs. We performed extensive feature generation and developed predictive models using logistic regression, boosted decision trees and neural networks. Based on the decision tree model, we performed a detailed feature importance analysis and subgroup analysis, with an emphasis on drug classes. Results The boosted decision tree model achieved an overall accuracy of 67.6% and an area under the curve-score of 0.74; the neural network and logistic regression models performed 0.4 and 1.9% worse, respectively. Feature engineering played a key role in capturing temporal patterns in the data. The number of features was reduced from 747 to 36 with only a 0.5% loss in the accuracy. In addition to hospitalisation and outpatient physician visits, 6 drug classes and the mode of drug administration were among the most important features. Patient subgroups with a high probability of increase (up to 88%) and decrease (up to 92%) were identified. Conclusions Pharmacotherapy provides important information for predicting cost increases in the total population. Moreover, its relative importance increases in combination with other features, including health care utilisation.

scholarly journals Prospects for the application of a new logistic regression model for the differential diagnosis of prostate cancer

2021 ◽  
Vol 99 (2) ◽  
pp. 98-102
Author(s):  
S. V. Ponkratov ◽  
I. B. Oleksjuk ◽  
K. L. Kozlov ◽  
A. V. Oleksjuk
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Logistic Regression Model ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Modern Medicine ◽  
Ultrasound Investigation

The differential diagnostic of prostate cancer is the actual task of modern medicine. The existing methods lack accuracy and specificity. It’s the reason of hyper- or hypo-diagnostic of this disease. We developed and tested the new logistic regression model for diagnostic of prostate cancer in men of various age. The model includes age, the volume of prostate, concentration of prostate specific antigen (PCA), 2-pro-PCA in the blood, the presence of the concretion revealed during digital rectal investigation of prostate and hypoechogenic area in transrectal ultrasound investigation. The model was tested in 114 patients. It has shown the higher accuracy and specificity of the new regression model in comparison to other methods of differential diagnostic of prostate cancer.

Intravoxel Incoherent Motion for Differentiating Spinal Metastasis: A Pilot Study

2021 ◽  
Author(s):  
Yuan Li ◽  
Xiaoying Xing ◽  
Enlong Zhang ◽  
Siyuan Qin ◽  
Huishu Yuan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Diffusion Coefficient ◽  
Regression Model ◽  
Diagnostic Performance ◽  
Logistic Regression Model ◽  
Spinal Metastasis ◽  
Exponential Model ◽  
Intravoxel Incoherent Motion ◽  
Model Parameters ◽  
Tuberculous Spondylitis

Abstract Background: To investigate the value of an intravoxel incoherent motion (IVIM) MRI for discriminating spinal metastasis from tuberculous spondylitis.Methods: This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 tuberculous spondylitis. All patients underwent IVIM MRI at 3.0T before treatment. The IVIM parameters including single-index model ( Apparent diffusion coefficient (ADC)-stand), double exponential model (ADCslow，ADCfast and f) and stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α) were acquired. Two radiologists separately measured these parameters for each lesion through drawing region of interest. Receiver operating characteristic (ROC) and the area under the ROC curve analysis was used to evaluate the diagnostic performance. Each parameter was substituted into the Logistic regression model to determine the meaningful parameters, and the combined diagnostic performance was evaluated.Results: The ADCfast and f showed significant differences between spinal metastasis and tuberculous spondylitis. (for all, p < 0.05). The Logistic regression model results showed that ADCfast and f were independent factors affecting the conclusion (P<0.05). The AUC values of ADCfast and f were 0.823 (95%CI:0.719 to 0.927) and 0.876 (95%CI: 0.782 to 0.969), respectively. ADCfast combined with f showed the highest AUC value of 0.925 (95%CI: 0.858 to 0.992). Additional significant differences were found in ADCstand, ADCslow, DDC and α among different metastasis type.Conclusions: IVIM MR imaging may be helpful for differentiating spinal metastasis from tuberculous spondylitis and may be used to detect the origin tumor for those patients who could not identify primary tumors, and provide help for clinical treatment.

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