scholarly journals Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 202
Author(s):  
Ge Gao ◽  
Hongxin Wang ◽  
Pengbin Gao
Keyword(s):  
Credit Risk ◽  
Evaluation System ◽  
Predictive Accuracy ◽  
Assessment System ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fusion Model ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
The Government

In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs’ credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions.

scholarly journals Interpretable Machine Learning for Early Neurological Deterioration Prediction in Atrial Fibrillation-Related Stroke

2021 ◽  
Author(s):  
Seong Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi O ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

Abstract We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multi-center prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.778, 95% CI, 0.726 - 0.830). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.

scholarly journals Convolutional Neural Network Classifies Pathological Voice Change in Laryngeal Cancer with High Accuracy

2020 ◽  
Vol 9 (11) ◽  
pp. 3415
Author(s):  
HyunBum Kim ◽  
Juhyeong Jeon ◽  
Yeon Jae Han ◽  
YoungHoon Joo ◽  
Jonghwan Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensitivity And Specificity ◽  
Laryngeal Cancer ◽  
Healthy Subjects ◽  
Gradient Boosting ◽  
Support Vector ◽  
Vowel Sound ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Voice changes may be the earliest signs in laryngeal cancer. We investigated whether automated voice signal analysis can be used to distinguish patients with laryngeal cancer from healthy subjects. We extracted features using the software package for speech analysis in phonetics (PRAAT) and calculated the Mel-frequency cepstral coefficients (MFCCs) from voice samples of a vowel sound of /a:/. The proposed method was tested with six algorithms: support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosted machine (LGBM), artificial neural network (ANN), one-dimensional convolutional neural network (1D-CNN) and two-dimensional convolutional neural network (2D-CNN). Their performances were evaluated in terms of accuracy, sensitivity, and specificity. The result was compared with human performance. A total of four volunteers, two of whom were trained laryngologists, rated the same files. The 1D-CNN showed the highest accuracy of 85% and sensitivity and sensitivity and specificity levels of 78% and 93%. The two laryngologists achieved accuracy of 69.9% but sensitivity levels of 44%. Automated analysis of voice signals could differentiate subjects with laryngeal cancer from those of healthy subjects with higher diagnostic properties than those performed by the four volunteers.

Landslide Susceptibility Analysis using Gradient Boosting Models: A Case Study in Penang Island, Malaysia

2021 ◽  
pp. 22-37
Author(s):  
Han Gao ◽  
Pei Shan Fam ◽  
Lea Tien Tay ◽  
Heng Chin Low
Keyword(s):  
Feature Selection ◽  
Landslide Susceptibility ◽  
Roc Curves ◽  
Spatial Prediction ◽  
Prediction Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Prediction Ability ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Tree-based gradient boosting (TGB) models gain popularity in various areas due to their powerful prediction ability and fast processing speed. This study aims to compare the landslide spatial prediction performance of TGB models and non-tree-based machine learning (NML) models in Penang Island, Malaysia. Two specific instances of TGB models, eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) and two specific instances of NML models, artificial neural network (ANN) and support vector machine (SVM), are applied to make predictions of landslide susceptibility. Feature selection and oversampling techniques are considered to improve the prediction performance as well. The results are analyzed and discussed mainly based on receiver operating characteristic (ROC) curves as well as the area under the curves (AUC). The results show that TGB models give better prediction performance compared to NML models, no matter what the sample size is. The TGB models’ performances are improved when training with the dataset considering either feature selection or oversampling techniques. The highest AUC value of 0.9525 is obtained from the combination of XGBoost and SMOTE. The landslide susceptibility maps (LSMs) produced by XGBoost and LightGBM can provide valuable information in landslide management and mitigation in Penang Island, Malaysia.

scholarly journals Application of Machine-Learning-Based Fusion Model in Visibility Forecast: A Case Study of Shanghai, China

2021 ◽  
Vol 13 (11) ◽  
pp. 2096
Author(s):  
Zhongqi Yu ◽  
Yuanhao Qu ◽  
Yunxin Wang ◽  
Jinghui Ma ◽  
Yu Cao
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Eastern China ◽  
Prediction Method ◽  
Sampling Technique ◽  
Environmental Modeling ◽  
Gradient Boosting ◽  
Fusion Model ◽  
Light Gradient ◽  
Extreme Gradient Boosting

A visibility forecast model called a boosting-based fusion model (BFM) was established in this study. The model uses a fusion machine learning model based on multisource data, including air pollutants, meteorological observations, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and an operational regional atmospheric environmental modeling System for eastern China (RAEMS) outputs. Extreme gradient boosting (XGBoost), a light gradient boosting machine (LightGBM), and a numerical prediction method, i.e., RAEMS were fused to establish this prediction model. Three sets of prediction models, that is, BFM, LightGBM based on multisource data (LGBM), and RAEMS, were used to conduct visibility prediction tasks. The training set was from 1 January 2015 to 31 December 2018 and used several data pre-processing methods, including a synthetic minority over-sampling technique (SMOTE) data resampling, a loss function adjustment, and a 10-fold cross verification. Moreover, apart from the basic features (variables), more spatial and temporal gradient features were considered. The testing set was from 1 January to 31 December 2019 and was adopted to validate the feasibility of the BFM, LGBM, and RAEMS. Statistical indicators confirmed that the machine learning methods improved the RAEMS forecast significantly and consistently. The root mean square error and correlation coefficient of BFM for the next 24/48 h were 5.01/5.47 km and 0.80/0.77, respectively, which were much higher than those of RAEMS. The statistics and binary score analysis for different areas in Shanghai also proved the reliability and accuracy of using BFM, particularly in low-visibility forecasting. Overall, BFM is a suitable tool for predicting the visibility. It provides a more accurate visibility forecast for the next 24 and 48 h in Shanghai than LGBM and RAEMS. The results of this study provide support for real-time operational visibility forecasts.

scholarly journals A stacked generalization ensemble model for optimization and prediction of the gas well rate of penetration: a case study in Xinjiang

2021 ◽  
Author(s):  
Naipeng Liu ◽  
Hui Gao ◽  
Zhen Zhao ◽  
Yule Hu ◽  
Longchen Duan
Keyword(s):  
Pearson Correlation ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ensemble Model ◽  
Rate Of Penetration ◽  
Gas Drilling ◽  
Light Gradient ◽  
Stacked Generalization ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting

AbstractIn gas drilling operations, the rate of penetration (ROP) parameter has an important influence on drilling costs. Prediction of ROP can optimize the drilling operational parameters and reduce its overall cost. To predict ROP with satisfactory precision, a stacked generalization ensemble model is developed in this paper. Drilling data were collected from a shale gas survey well in Xinjiang, northwestern China. First, Pearson correlation analysis is used for feature selection. Then, a Savitzky-Golay smoothing filter is used to reduce noise in the dataset. In the next stage, we propose a stacked generalization ensemble model that combines six machine learning models: support vector regression (SVR), extremely randomized trees (ET), random forest (RF), gradient boosting machine (GB), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGB). The stacked model generates meta-data from the five models (SVR, ET, RF, GB, LightGBM) to compute ROP predictions using an XGB model. Then, the leave-one-out method is used to verify modeling performance. The performance of the stacked model is better than each single model, with R2 = 0.9568 and root mean square error = 0.4853 m/h achieved on the testing dataset. Hence, the proposed approach will be useful in optimizing gas drilling. Finally, the particle swarm optimization (PSO) algorithm is used to optimize the relevant ROP parameters.

scholarly journals Interpretable Machine Learning Model to Predict Rupture of Small Intracranial Aneurysms and Facilitate Clinical Decision

2021 ◽  
Author(s):  
WeiGen Xiong ◽  
TingTing Chen ◽  
ZhiHong Zhao ◽  
XueMei Li ◽  
YaJie Shan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intracranial Aneurysms ◽  
External Validation ◽  
Maximum Size ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Support Vector ◽  
Rupture Risk ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Abstract Estimating the rupture risk of small intracranial aneurysms (IAs) to determine whether to treat is difficult but crucial. We aimed to construct and external validation a convenient machine learning (ML) model for assessing the rupture risk of small IAs.1004 patients with small IAs recruited from two hospitals were included in our retrospective research. The patients at hospital 1 were stratified into training (70%) and internal validation set (30%) randomly, and the patients at hospital 2 were used for external validation. We selected predictive features using the least absolute shrinkage and selection operator (LASSO) method, and constructed five ML models applying diverse algorithms including random forest classifier (RFC), categorical boosting (CatBoost), support vector machine (SVM) with linear kernel, light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost). The Shapley Additive Explanations (SHAP) analysis provided interpretation for the best ML model.The training, internal and external validation cohorts included 658, 282, and 64 IAs, respectively. The best performance was presented by SVM as AUC of 0.817 in the internal [95% confidence interval (CI), 0.769-0.866] and 0.893 in the external (95% CI, 0.808-0.979) validation cohorts, overperformed than the PHASES score significantly (all P < 0.001). SHAP analysis showed maximum size, location and irregular shape were the top three important features to predict rupture. Our SVM model based on readily accessible features presented satisfying ability of discrimination in predicting the rupture IAs with small size. Morphological parameters made important contributions to prediction result.

scholarly journals Prediction of Radiation Pneumonitis With Machine Learning in Stage III Lung Cancer: A Pilot Study

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Melek Yakar ◽  
Durmus Etiz ◽  
Muzaffer Metintas ◽  
Guntulu Ak ◽  
Ozer Celik
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Radiation Pneumonitis ◽  
Stage Iii ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Volume Number ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Background: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy (RT). As risk factors in the development of RP, patient and tumor characteristics, dosimetric parameters, and treatment features are intertwined, and it is not always possible to associate RP with a single parameter. This study aimed to determine the algorithm that most accurately predicted RP development with machine learning. Methods: Of the 197 cases diagnosed with stage III lung cancer and underwent RT and chemotherapy between 2014 and 2020, 193 were evaluated. The CTCAE 5.0 grading system was used for the RP evaluation. Synthetic minority oversampling technique was used to create a balanced data set. Logistic regression, artificial neural networks, eXtreme Gradient Boosting (XGB), Support Vector Machines, Random Forest, Gaussian Naive Bayes and Light Gradient Boosting Machine algorithms were used. After the correlation analysis, a permutation-based method was utilized for as a variable selection. Results: RP was seen in 51 of the 193 cases. Parameters affecting RP were determined as, total(t)V5, ipsilateral lung Dmax, contralateral lung Dmax, total lung Dmax, gross tumor volume, number of chemotherapy cycles before RT, tumor size, lymph node localization and asbestos exposure. LGBM was found to be the algorithm that best predicted RP at 85% accuracy (confidence interval: 0.73-0.96), 97% sensitivity, and 50% specificity. Conclusion: When the clinical and dosimetric parameters were evaluated together, the LGBM algorithm had the highest accuracy in predicting RP. However, in order to use this algorithm in clinical practice, it is necessary to increase data diversity and the number of patients by sharing data between centers.

scholarly journals Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seong-Hwan Kim ◽  
Eun-Tae Jeon ◽  
Sungwook Yu ◽  
Kyungmi Oh ◽  
Chi Kyung Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Neurological Deterioration ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting ◽  
Early Neurological Deterioration ◽  
Feature Importance

AbstractWe aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.

scholarly journals Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 203
Author(s):  
I-Jung Tsai ◽  
Wen-Chi Shen ◽  
Chia-Ling Lee ◽  
Horng-Dar Wang ◽  
Ching-Yu Lin
Keyword(s):  
Machine Learning ◽  
Bladder Cancer ◽  
Clinical Laboratory ◽  
Screening Method ◽  
Laboratory Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Urinary Cytology ◽  
Light Gradient ◽  
Extreme Gradient Boosting

Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.

scholarly journals An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7367
Author(s):  
Mohamed Chaibi ◽  
EL Mahjoub Benghoulam ◽  
Lhoussaine Tarik ◽  
Mohamed Berrada ◽  
Abdellah El Hmaidi
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Predictive Accuracy ◽  
Sunshine Duration ◽  
Predictive Performance ◽  
Global Solar Radiation ◽  
Gradient Boosting ◽  
Support Vector ◽  
Light Gradient ◽  
Testing Stage

Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation techniques to explain and improve the predictions of ML models. We compared first the predictive performance of Light Gradient Boosting (LightGBM) with three benchmark models, including multilayer perceptron (MLP), multiple linear regression (MLR), and support-vector regression (SVR), for estimating the global solar radiation (H) in the city of Fez, Morocco. Then, the predictions of the most accurate model were explained by two model-agnostic explanation techniques: permutation feature importance (PFI) and Shapley additive explanations (SHAP). The results indicated that LightGBM (R2 = 0.9377, RMSE = 0.4827 kWh/m2, MAE = 0.3614 kWh/m2) provides similar predictive accuracy as SVR, and outperformed MLP and MLR in the testing stage. Both PFI and SHAP methods showed that extraterrestrial solar radiation (H0) and sunshine duration fraction (SF) are the two most important parameters that affect H estimation. Moreover, the SHAP method established how each feature influences the LightGBM estimations. The predictive accuracy of the LightGBM model was further improved slightly after re-examination of features, where the model combining H0, SF, and RH was better than the model with all features.

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