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World Scientific Research Journal 2023年第7期9卷 47-56页

作者： Jiaxiang Liang Tianqian Huang Ying Xiao

In this paper,A‐share listed enterprises in the pharmaceutical manufacturing industry that were punished for financial fraud during 2015‐2019 and their matching non‐fraud enterprises were selected as the research *** with the fraud triangle theory and existing research results,the logistic regression model was used to make an empirical analysis of the relationship between financial risk and financial *** results show that the greater the financial risk,the greater the possibility of financial fraud.

关键词： Financial malpractice Financial risk logistic regression model Pharmaceutical manufacturing industry

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A random forest algorithm-based prediction model for moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia

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BMC ANESTHESIOLOGY 2023年第1期23卷 361-361页

作者： Shi, Gaoxiang Liu, Geliang Gao, Qichao Zhang, Shengxiao Wang, Qi Wu, Li He, Peifeng Yu, Qi Shanxi Med Univ Sch Basic Med Sci Taiyuan 030001 Peoples R China Shanxi Med Univ Inst Med Data Sci Taiyuan Peoples R China Shanxi Med Univ Shanxi Key Lab Big Data Clin Decis Taiyuan Peoples R China Shanxi Med Univ Shanxi Bethune Hosp Tongji Shanxi Hosp Shanxi Acad Med SciHosp 3Dept Anesthesiol Taiyuan Peoples R China Shanxi Med Univ Sch Management Taiyuan Peoples R China Shanxi Med Univ Dept Rheumatol Hosp 2 Taiyuan Peoples R China Shanxi Med Univ Key Lab Cellular Physiol Minist Educ Taiyuan Peoples R China

BackgroundPostoperative pain is one of the most common complications after surgery. In order to detect early and intervene in time for moderate to severe postoperative pain, it is necessary to identify risk factors and construct clinical prediction models. This study aimed to identify significant risk factors and establish a better-performing model to predict moderate to severe acute postoperative pain after orthopedic surgery under general *** who underwent orthopedic surgery under general anesthesia were divided into patients with moderate to severe pain group (group P) and patients without moderate to severe pain group (group N) based on VAS scores. The features selected by Lasso regression were processed by the random forest and multivariate logistic regression models to predict pain outcomes. The classification performance of the two models was evaluated through the testing set. The area under the curves (AUC), the accuracy of the classifiers, and the classification error rate for both classifiers were calculated, the better-performing model was used to predict moderate to severe acute postoperative pain after orthopedic surgery under general *** total of 327 patients were enrolled in this study (228 in the training set and 99 in the testing set). The incidence of moderate to severe postoperative pain was 41.3%. The random forest model revealed a classification error rate of 25.2% and an AUC of 0.810 in the testing set. The multivariate logistic regression model revealed a classification error rate of 31.3% and an AUC of 0.764 in the testing set. The random forest model was chosen for predicting clinical outcomes in this study. The risk factors with the greatest and second contribution were immobilization and duration of surgery, *** random forest model can be used to predict moderate to severe acute postoperative pain after orthopedic surgery under general anesthesia, which is of potential clinical

关键词： Random forest model logistic regression model Machine learning Pain prediction Analgesia

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The effect of age on entheseal changes: A study of modifications at appendicular attachment sites in a large sample of identified human skeletons

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INTERNATIONAL JOURNAL OF OSTEOARCHAEOLOGY 2023年第3期33卷 389-401页

作者： Villotte, Sebastien Santos, Frederic Univ Paris Cite CNRS MNHNMusee Homme Eco AnthropolUMR7206 17 Pl Trocadero F-75116 Paris France Royal Belgian Inst Nat Sci Quaternary Environm & Humans ODEarth & Hist life Rue Vautierstraat 29 B-1000 Brussels Belgium Univ Liege Unite Rech Art Archeol Patrimoine Allee Six Aout 10 B-4000 Liege Belgium Univ Bordeaux CNRS MCC UMR 5199PACEA Pessac France

Entheseal changes (EC) are frequent in older individuals, and age at death is the main variable explaining the distribution of EC stages in adult skeletal samples. While this is already well known, the underlying process has not been studied in biological anthropology to date. The aim of the article is to describe the relationship between age and stages of EC in detail for 30 appendicular entheses in a large sample of adult skeletons (N = 721) with known age at death and sex, using several statistical tools (such as Spearman's rank correlation coefficients, logistic regression models, adjusted standardized residuals, and generalized odds ratios). The analyses indicate a moderate to strong relationship between age at death and EC for all the appendicular attachment sites, but also striking differences between fibrous and fibrocartilaginous entheseal attachment sites. These differences are discussed in light of the available biomedical data.

关键词： age at death fibrous enthesis fibrocartilaginous enthesis musculoskeletal stress markers entheseal changes logistic regression model adjusted standardized residual generalized odds ratio fisher exact test log-linear model mode mosaic plot relative stage frequency Spearman's rank correlation coefficient stacked bar plot

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Analyzing Factors Affecting Credit Card Fraud: Four model-based Approach 23

Analyzing Factors Affecting Credit Card Fraud: Four Model-ba...

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Proceedings of the 2023 5th International Conference on Big Data Engineering

作者： Xuefei Xu Kailin Chen University of North Texas USA City University of Hong Kong China

ISBN: (纸本)9798400708695

With the proliferation of the internet, credit card fraud has become a pressing issue, leading to substantial financial losses and undermining trust among consumers. This research aims to elucidate the determinants associated with credit card fraud. By importing and cleansing two databases from Kaggle, we constructed two heatmaps for comparison, subsequently selecting the most suitable database for further analysis. We then established four models: Linear regression model, Random Forest classifier, logistic regression model, and Decision Tree model. By comparing the confusion matrices and ROC curves of each model, the Linear regression model emerged as the most proficient. Ultimately, three highly correlative factors were identified in relation to credit card fraud: High-risk country, Total number of declines per day, and 6-month chargeback frequency. The findings from this research pave the way for bolstering financial security, enhancing the efficacy of fraud detection, and thereby mitigating potential losses for consumers.

关键词： Decision Tree model Linear regression model logistic regression model Machine Learning in Fraud Detection Random Forest model

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Diagnostics of multiple group influential observations for logistic regression models

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2019年第16期89卷 3118-3136页

作者： Coskun, Burcin Alpu, O. Univ Eskisehir Osmangazi Inst Educ Sci Eskisehir Turkey Eskisehir Osmangazi Univ Dept Stat TR-26480 Eskisehir Turkey

In this paper, two new multiple influential observation detection methods, *** and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.

关键词： Multiple group logistic regression diagnostics suspicious observations influential observations logistic regression model multiple influential observations modified Cook's distance generalized Cook's distance generalized difference in fits generalized standardized Pearson residuals Cook's distance

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Expression of EMT-related genes in lymph node metastasis in endometrial cancer: a TCGA-based study (vol 21, 55, 2023)

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WORLD JOURNAL OF SURGICAL ONCOLOGY 2023年第1期21卷 55-55页

作者： Li, He Wang, Junzhu Li, Liwei Zhao, Luyang Wang, Zhiqi Peking Univ Peoples Hosp Dept Obstet & Gynecol 11 Xizhimen South St Beijing 100044 Peoples R China Peking Univ Sch Govt Big Data & Publ Policy Lab Beijing Peoples R China

Background Endometrial cancer (EC) with metastasis in pelvic/para-aortic lymph nodes suggests an unsatisfactory prognosis. Nevertheless, there is still rare literature focusing on the role of epithelial-mesenchymal transition (EMT) in lymph node metastasis (LNM) in EC. Methods Transcriptional data were derived from the TCGA database. Patients with stage IA–IIIC2 EC were included, constituting the LN-positive and LN-negative groups. To evaluate the extent of EMT, an EMT signature composed of 315 genes was adopted. The EMT-related genes (ERGs) were obtained from the dbEMT2 database, and the differentially expressed ERGs (DEERGs) between these two groups were screened. On the basis of DEERGs, pathway analysis was carried out. We eventually adopted the logistic regression model to build an ERG-based gene signature with predictive value for LNM in EC. Results A total of 498 patients were included, with 75 in the LN-positive group. Median EMT score of tumor tissues from LN-negative group was − 0.369, while that from the LN-positive group was − 0.296 (P < 0.001), which clearly exhibited a more mesenchymal phenotype for LNM cases on the EMT continuum. By comparing expression profiles, 266 genes were identified as DEERGs, in which 184 were upregulated and 82 were downregulated. In pathway analysis, various EMT-related pathways were enriched. DEERGs shared between molecular subtypes were comparatively few. The ROC curve and logistic regression analysis screened 7 genes with the best performance to distinguish between the LN-positive and LN-negative group, i.e., CIRBP, DDR1, F2RL2, HOXA10, PPARGC1A, SEMA3E, and TGFB1. A logistic regression model including the 7-gene-based risk score, age, grade, myometrial invasion, and histological subtype was built, with an AUC of 0.850 and a favorite calibration (P = 0.074). In the validation dataset composed of 83 EC patients, the model exhibited a satisfactory predictive value and was well-calibrated (P = 0.42). Conclusion The EMT status

关键词： Endometrial cancer Lymph node metastasis Epithelial-mesenchymal transition The cancer genome atlas Pathway Gene signature logistic regression model Nomogram

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The association of trace elements with arthritis in US adults: NHANES 2013-2016

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JOURNAL OF TRACE ELEMENTS IN MEDICINE AND BIOLOGY 2023年第1期76卷 127122页

作者： Guan, Tong Wu, Zhen Xu, Changsha Su, Gang Suqian Ctr Dis Control & Prevent Suqian Jiangsu Peoples R China Suqian Ctr Dis Control & Prevent 8 Renmin Ave Suqian 223899 Peoples R China

Background: Arthritis is a common chronic disease, and is a major cause of disability and chronic pain in adults. Considering inflammatory responses is closely related with trace elements (TEs), the role of TEs in arthritis has attracted much attention. This study aimed to assess the association between TEs and ***: Concentrations of TEs in whole blood [cadmium (Cd), lead (Pb), mercury (Hg), selenium (Se), and manganese (Mn)] and serum [copper (Cu) and zinc (Zn)] were measured in adults who participated in the US National Health and Nutrition Examination Survey. logistic regression model and Bayesian kernel machine regression model were used to explore the association between TEs and arthritis. Results: The levels of five TEs (Pb, Hg, Cd, Se, and Cu) in the arthritis group changed significantly. Three TEs were found to be associated with an increased risk of arthritis: Pb [OR (95% CI): 2.96 (2.18, 4.03), p-value for trend (P- t) < 0.001], Cd [OR (95% CI): 2.28 (1.68, 3.11), P-t < 0.001], Cu [OR (95% CI): 2.05 (1.53, 2.76), P-t < 0.001]. The Relative Excess Risk of Interaction was 0.35 (95% CI: 0.06-0.65) and 0.38 (95% CI: 0.11-0.64), respectively, suggesting that Hg ions and Se ions have positive additional interactions with alcohol consumption, which reduced the risk of arthritis. Subgroup analysis showed that Pb ions and Cd ions were significantly correlated with osteoarthritis and rheumatoid ***: Elevated concentrations of Pb, Cd, and Cu were associated with increased risk of arthritis. Drinking with high levels of Hg or Se may be a protective factor for arthritis. Future studies are warranted to validate these findings in prospective studies.

关键词： Arthritis Trace elements Alcohol consumption logistic regression model BKMR model

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Reliability Evaluation and Prediction Method with Small Samples

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGE...

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INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2023年第4期8卷 560-580页

作者： Dui, Hongyan Dong, Xinghui Tao, Junyong Zhengzhou Univ Sch Management Zhengzhou 450001 Henan Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Lab Sci & Technol Integrated Logist Support Changsha 410073 Peoples R China

How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities.

关键词： Reliability evaluation Cox model logistic regression model Reliability prediction

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The Impact and Development of Modern Internet Financial Technology on the Traditional Financial Industry

The Impact and Development of Modern Internet Financial Tech...

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作者： Kaijun Zhang Faculty of Information Science and Engineering Ocean University of China

The financial technology industry has experienced significant growth due to the rapid integration of artificial intelligence,big data,cloud computing,Internet of Things,blockchain,and other information technologies into financial *** growth can be attributed to the emergence of a new wave of scientific and technological revolution and industrial *** integration has facilitated the progress of the financial technology *** study employs the logistic regression model to examine the effects on the conventional financial *** author's research provides a detailed examination of the developmental trajectory of Internet finance and financial technology,while also assessing the present challenges faced by Internet finance and its repercussions on the conventional financial *** a comprehensive examination of the text in its entirety,a concise overview of the historical and prospective financial environment might yield a more perceptive comprehension of the ramifications of emerging technologies on the conventional financial *** present study aims to provide a thorough examination of the paper,with the expectation that it will enable individuals to get a more lucid understanding of the overarching circumstances.

关键词： fintech logistic regression model internet finance traditional fintech industry

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Construction and validation of machine learning models for sepsis prediction in patients with acute pancreatitis

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BMC SURGERY 2023年第1期23卷 267-267页

作者： Liu, Fei Yao, Jie Liu, Chunyan Shou, Songtao Tianjin Med Univ Gen Hosp Dept Emergency Med 154 Anshan Rd Tianjin 300052 Peoples R China Hebei North Univ Dept Anesthesiol Affiliated Hosp 1 Zhangjiakou 075000 Hebei Peoples R China Hebei North Univ Dept Intens Care Unit Affiliated Hosp 1 Zhangjiakou 075000 Hebei Peoples R China

BackgroundThis study aimed to construct predictive models for the risk of sepsis in patients with Acute pancreatitis (AP) using machine learning methods and compared optimal one with the logistic regression (LR) model and scoring *** this retrospective cohort study, data were collected from the Medical Information Mart for Intensive Care III (MIMIC III) database between 2001 and 2012 and the MIMIC IV database between 2008 and 2019. Patients were randomly divided into training and test sets (8:2). The least absolute shrinkage and selection operator (LASSO) regression plus 5-fold cross-validation were used to screen and confirm the predictive factors. Based on the selected predictive factors, 6 machine learning models were constructed, including support vector machine (SVM), K-nearest neighbour (KNN), multi-layer perceptron (MLP), LR, gradient boosting decision tree (GBDT) and adaptive enhancement algorithm (AdaBoost). The models and scoring systems were evaluated and compared using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the area under the curve (AUC).ResultsA total of 1, 672 patients were eligible for participation. In the training set, 261 AP patients (19.51%) were diagnosed with sepsis. The predictive factors for the risk of sepsis in AP patients included age, insurance, vasopressors, mechanical ventilation, Glasgow Coma Scale (GCS), heart rate, respiratory rate, temperature, SpO2, platelet, red blood cell distribution width (RDW), International Normalized Ratio (INR), and blood urea nitrogen (BUN). The AUC of the GBDT model for sepsis prediction in the AP patients in the testing set was 0.985. The GBDT model showed better performance in sepsis prediction than the LR, systemic inflammatory response syndrome (SIRS) score, bedside index for severity in acute pancreatitis (BISAP) score, sequential organ failure assessment (SOFA) score, quick-SOFA (qSOFA), and simplified acute physiology s

关键词： Machine learning models logistic regression model Scoring systems sepsis Acute pancreatitis

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