Kosdaq Enterprises insolvency prediction model study on the comparative analysis of Discrimination - A logistic regression model and multivariate discriminant function center -

2016 ◽  
Vol 25 ◽  
pp. 241
Author(s):  
Chang Ho An
Keyword(s):  
Logistic Regression ◽  
Comparative Analysis ◽  
Prediction Model ◽  
Regression Model ◽  
Discriminant Function ◽  
Logistic Regression Model ◽  
Model Study ◽  
Multivariate Discriminant

scholarly journals Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

scholarly journals Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm

2020 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Yan-Ming Chu ◽  
Cheng-Jian Cao ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Abstract Background: Metabolic syndrome (MS) screening is important for the early detection of occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.

Establishment of Prediction Models for venous thromboembolism in non-oncological urological inpatients--a single center experience

2020 ◽  
Author(s):  
Kaixuan Li ◽  
Haozhen Li ◽  
Quan Zhu ◽  
Ziqiang Wu ◽  
Zhao Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Venous Thromboembolism ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Predictive Ability ◽  
Multivariate Logistic Regression Model ◽  
Single Center Experience

Abstract Background To establish prediction models for venous thromboembolism (VTE) in non-oncological urological inpatients. Methods A retrospective analysis of 1453 inpatients was carried out and the risk factors for VTE had been clarified our previous studies. Results Risk factors included the following 5 factors: presence of previous VTE (X1), presence of anticoagulants or anti-platelet agents treatment before admission (X2), D-dimer value (≥ 0.89 µg/ml, X3), presence of lower extremity swelling (X4), presence of chest symptoms (X5). The logistic regression model is Logit (P) = − 5.970 + 2.882 * X1 + 2.588 * X2 + 3.141 * X3 + 1.794 * X4 + 3.553 * X5. When widened the p value to not exceeding 0.1 in multivariate logistic regression model, two addition risk factors were enrolled: Caprini score (≥ 5, X6), presence of complications (X7). The prediction model turns into Logit (P) = − 6.433 + 2.696 * X1 + 2.507 * X2 + 2.817 * X3 + 1.597 * X4 + 3.524 * X5 + 0.886 * X6 + 0.963 * X7. Internal verification results suggest both two models have a good predictive ability, but the prediction accuracy turns to be both only 43.0% when taking the additional 291 inpatients’ data in the two models. Conclusion We built two similar novel prediction models to predict VTE in non-oncological urological inpatients. Trial registration: This trial was retrospectively registered at http://www.chictr.org.cn/index.aspx under the public title“The incidence, risk factors and establishment of prediction model for VTE n urological inpatients” with a code ChiCTR1900027180 on November 3, 2019. (Specific URL to the registration web page: http://www.chictr.org.cn/showproj.aspx?proj=44677).

scholarly journals Two-stage prediction model for in-hospital mortality of patients with influenza infection

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chan-Wa Cheong ◽  
Chien-Lin Chen ◽  
Chih-Huang Li ◽  
Chen-June Seak ◽  
Hsiao-Jung Tseng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Critical Care ◽  
Critical Illness ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Stage I ◽  
Multiple Logistic Regression ◽  
Two Stage

Abstract Background Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality. Methods This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model. Results In the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model’s S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively. Conclusions The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.

scholarly journals Two-stage Prediction Model for in-hospital Mortality of Patients With Influenza Infection

2021 ◽  
Author(s):  
Chan-Wa Cheong ◽  
Chien-Lin Chen ◽  
Chih-Huang Li ◽  
Chen-June Seak ◽  
Hsiao-Jung Tseng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Critical Care ◽  
Critical Illness ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Stage I ◽  
Multiple Logistic Regression ◽  
Two Stage

Abstract Background: Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality.Methods: This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model.Results: In the present study, 1,680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model’s S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively.Conclusions: The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.

scholarly journals Mediastinal/hilar lymphadenopathy: A possibly overlooked variable in the classification of lung nodules

2021 ◽  
Author(s):  
qingxia fan
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Lung Nodule ◽  
Added Value ◽  
Lung Nodules ◽  
Hilar Lymphadenopathy

Abstract Background Clinical prediction models to classify lung nodules often exclude patients with mediastinal/hilar lymphadenopathy, although the presence of mediastinal/hilar lymphadenopathy does not always indicate malignancy. Herein, we developed and validated a multimodal prediction model for lung nodules in which patients with mediastinal/hilar lymphadenopathy were included. Methods A total of 359 patients with pulmonary nodules were considered for enrollment in the study. We developed and validated a logistic regression model including patients with mediastinal/hilar lymphadenopathy. Discrimination of the model was assessed by area under the operating curve. Goodness of fit was performed via the Hosmer-Lemeshow test, and a nomogram of the logistic regression model was drawn. Results There were 311 cases included in the final analysis. A logistic regression model was developed and validated. There were nine independent variables included in the model. The AUC of the training and validation sets was 0.93 (95% CI, 0.90–0.97) and 0.91 (95% CI, 0.85–0.98), respectively. In the validation set with or without mediastinal/hilar lymphadenopathy, the AUC was 0.95 (95% CI, 0.90–0.99) and 0.91 (95%CI, 0.87–0.95), respectively. The Hosmer-Lemeshow goodness-of-fit statistic was 0.22. A nomogram was drawn to visualize the model. Conclusions We developed and validated a multimodal risk prediction model for lung nodules with excellent discrimination and calibration, regardless of the inclusion of mediastinal/hilar lymphadenopathy. This broadens the application of lung nodule prediction models. Furthermore, the presence of mediastinal/hilar lymphadenopathy added value for predicting lung nodule malignancy, highlighting the importance of this variable in clinical practice.

scholarly journals A Nomogram for Predicting Lymph Node Metastasis in Submucosal Colorectal Cancer

2017 ◽  
Vol 102 (3-4) ◽  
pp. 102-108
Author(s):  
Shiki Fujino ◽  
Norikatsu Miyoshi ◽  
Masayuki Ohue ◽  
Masayoshi Yasui ◽  
Keijiro Sugimura ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Medical Center ◽  
Univariate Analysis ◽  
Node Metastasis

In colorectal cancer (CRC), the possibility of lymph node (LN) metastasis is an important consideration when deciding on treatment. We developed a nomogram for predicting lymph node metastasis of submucosal (SM) CRC. The medical records of 509 patients with SM CRC from 1984 to 2012 were retrospectively investigated. All the patients underwent curative surgical resection at the Osaka Medical Center for Cancer and Cardiovascular Diseases. A total 113 patients with inadequate data were excluded. Using a group of 293 patients who underwent surgery from 1984 to 2008, a logistic regression model was used to develop a prediction model for LN metastasis. The prediction model was validated in an additional group of 103 patients who underwent surgery from 2009 to 2012. Univariate analysis of pathologic factors showed the influence of low histologic grade (muc, por, sig; P < 0.001), positive lymphatic invasion (P < 0.001), positive vascular invasion (P = 0.036), and tumor SM invasion depth (P = 0.098) in LN metastasis. Using these variables, a nomogram predicting LN metastasis was constructed using a logistic regression model with an area under the curve (AUC) of 0.717. The prediction model was validated by an external dataset in an independent patient group with an AUC of 0.920. We developed a novel and reliable nomogram predicting LN metastasis through the integration of 4 pathologic factors. This prediction model may help clinicians to decide on personalized treatment following endoscopic resection.

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