Financial Fraud Detection and Prediction in Listed Companies Using SMOTE and Machine Learning Algorithms.

This paper proposes a new method that can identify and predict financial fraud among listed companies based on machine learning. We collected 18,060 transactions and 363 indicators of finance, including 362 financial variables and a class variable. Then, we eliminated 9 indicators which were not related to financial fraud and processed the missing values. After that, we extracted 13 indicators from 353 indicators which have a big impact on financial fraud based on multiple feature selection models and the frequency of occurrence of features in all algorithms. Then, we established five single classification models and three ensemble models for the prediction of financial fraud records of listed companies, including LR, RF, XGBOOST, SVM, and DT and ensemble models with a voting classifier. Finally, we chose the optimal single model from five machine learning algorithms and the best ensemble model among all hybrid models. In choosing the model parameter, optimal parameters were selected by using the grid search method and comparing several evaluation metrics of models. The results determined the accuracy of the optimal single model to be in a range from 97% to 99%, and that of the ensemble models as higher than 99%. This shows that the optimal ensemble model performs well and can efficiently predict and detect fraudulent activity of companies. Thus, a hybrid model which combines a logistic regression model with an XGBOOST model is the best among all models. In the future, it will not only be able to predict fraudulent behavior in company management but also reduce the burden of doing so.

Development and External Validation of a Dynamic Nomogram With Potential for Risk Assessment of Ruptured Multiple Intracranial Aneurysms.

BACKGROUND AND PURPOSE: About 20.1% of intracranial aneurysms (IAs) carriers are multiple intracranial aneurysms (MIAs) patients with higher rupture risk and worse prognosis. A prediction model may bring some potential benefits. This study attempted to develop and externally validate a dynamic nomogram to assess the rupture risk of each IA among patients with MIA. METHOD: We retrospectively analyzed the data of 262 patients with 611 IAs admitted to the Hunan Provincial People's Hospital between November 2015 and November 2021. Multivariable logistic regression (MLR) was applied to select the risk factors and derive a nomogram model for the assessment of IA rupture risk in MIA patients. To externally validate the nomogram, data of 35 patients with 78 IAs were collected from another independent center between December 2009 and May 2021. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical utility. RESULT: Size, location, irregular shape, diabetes history, and neck width were independently associated with IA rupture. The nomogram showed a good discriminative ability for ruptured and unruptured IAs in the derivation cohort (AUC = 0.81; 95% CI, 0.774-0.847) and was successfully generalized in the external validation cohort (AUC = 0.744; 95% CI, 0.627-0.862). The nomogram was calibrated well, and the decision curve analysis showed that it would generate more net benefit in identifying IA rupture than the "treat all" or "treat none" strategies at the threshold probabilities ranging from 10 to 60% both in the derivation and external validation set. The web-based dynamic nomogram calculator was accessible on https://wfs666.shinyapps.io/onlinecalculator/. CONCLUSION: External validation has shown that the model was the potential to assist clinical identification of dangerous aneurysms after longitudinal data evaluation. Size, neck width, and location are the primary risk factors for ruptured IAs.

Development and assessment of machine learning models for predicting recurrence risk after endovascular treatment in patients with intracranial aneurysms.

Intracranial aneurysms (IAs) remain a major public health concern and endovascular treatment (EVT) has become a major tool for managing IAs. However, the recurrence rate of IAs after EVT is relatively high, which may lead to the risk for aneurysm re-rupture and re-bleed. Thus, we aimed to develop and assess prediction models based on machine learning (ML) algorithms to predict recurrence risk among patients with IAs after EVT in 6 months. Patient population included patients with IAs after EVT between January 2016 and August 2019 in Hunan Provincial People's Hospital, and an adaptive synthetic (ADASYN) sampling approach was applied for the entire imbalanced dataset. We developed five ML models and assessed the models. In addition, we used SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. A total of 425 IAs were enrolled into this study, and 66 (15.5%) of which recurred in 6 months. Among the five ML models, gradient boosting decision tree (GBDT) model performed best. The area under curve (AUC) of the GBDT model on the testing set was 0.842 (sensitivity: 81.2%; specificity: 70.4%). Our study firstly demonstrated that ML-based models can serve as a reliable tool for predicting recurrence risk in patients with IAs after EVT in 6 months, and the GBDT model showed the optimal prediction performance.

Comparison of pathological outcomes between robotic rectal cancer surgery and laparoscopic rectal cancer surgery: A meta-analysis based on seven randomized controlled trials.

OBJECTIVE: To compare the pathologic outcomes between robotic-assisted rectal cancer surgery (RRCS) and laparoscopic rectal cancer surgery (LRCS) based on randomized controlled trials (RCTs). METHODS: Electronic databases were searched from their inception to 7 October 2018, for RCTs involving a comparison between RRCS and LRCS. Positive circumferential resection margin (CRM), distance to the distal margin, proximal margin, the rate of complete mesorectal excision, and harvested lymph nodes were interesting of outcomes. RESULTS: The positive CRM, proximal margin, and rate of complete mesorectal excision were comparable between the two techniques. RRCS resulted in better outcomes for the distance to the distal margin (mean difference: 0.83 cm, 95% confidence interval, 0.29-1.37, P = .003). CONCLUSIONS: The pathologic outcomes associated with the two approaches were comparable, with RRCS showing better outcomes for the distance to the distal margin. However, additional well-designed studies are needed to assess whether the benefits of pathologic outcomes improve survival outcomes.

Assessment of prokaryotic collagen-like sequences derived from streptococcal Scl1 and Scl2 proteins as a source of recombinant GXY polymers.

Collagen triple helix, composed of the repeating Gly-Xaa-Yaa (GXY) sequence, is a structural element found in all multicellular animals and also in some prokaryotes. Long GXY polymers are highly regarded components used in food, cosmetic, biomedical, and pharmaceutical industries. In this study, we explore a new concept for the production of recombinant GXY polymers which are based on the sequence of "prokaryotic collagens", the streptococcal collagen-like proteins Scl1 and Scl2. Analysis of 50 Scl variants identified the amino acid distribution and GXY-repeat usage that are involved in the stabilization of the triple helix in Scls. Using circular dichroism spectroscopy and electron microscopy, we show that significantly different recombinant rScl polypeptides form stable, unhydroxylated homotrimeric triple helices that can be produced both intra- and extracellularly in the Escherichia coli. These rScl constructs containing 20 to 129 GXY repeats had mid-point melting temperatures between 32 and 39 degrees C. Altogether, Scl-derived collagens, which are different from the mammalian collagens, can form stable triple helices under physiological conditions and can be used for the production of recombinant GXY polymers with a wide variety of potential applications.