scholarly journals Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment

2019 ◽  
Vol 11 (5) ◽  
pp. 1327 ◽  
Author(s):  
Bei Zhou ◽  
Zongzhi Li ◽  
Shengrui Zhang ◽  
Xinfen Zhang ◽  
Xin Liu ◽  
...  
Keyword(s):  
Geometric Mean ◽  
Regression Tree ◽  
Classification Performance ◽  
Classification And Regression Tree ◽  
Factors Affecting ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Hit And Run ◽  
Reported Data ◽  
Cart Model

Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

scholarly journals Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kaizhou Huang ◽  
Feiyang Ji ◽  
Zhongyang Xie ◽  
Daxian Wu ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
High Risk ◽  
Hepatitis B ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Artificial Liver ◽  
Liver Support ◽  
Artificial Liver Support ◽  
Cart Analysis ◽  
Classification And Regression ◽  
Cart Model

Abstract Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.

scholarly journals INVESTIGATING FACTORS AFFECTING ROAD FREIGHT OVERLOADING THROUGH THE INTEGRATED USE OF BLR AND CART: A CASE STUDY IN CHINA

Transport ◽  
2020 ◽  
Vol 35 (3) ◽  
pp. 236-246
Author(s):  
Yikai Chen ◽  
Kai Wang ◽  
Yu Zhang ◽  
Renjia Luo ◽  
Shujun Yu ◽  
...  
Keyword(s):  
Operation Mode ◽  
Regression Tree ◽  
Binary Logistic Regression ◽  
Statistical Modelling ◽  
Road Transport ◽  
Classification And Regression Tree ◽  
Factors Affecting ◽  
Road Freight ◽  
Efficiency And Effectiveness ◽  
Freight Vehicles

Overloading of road freight vehicles accelerates road damage, creates unfair competition in the transport market, and increases safety risk. There is a dearth of research on the mining of data of highway Freight Weight (FW), and this paper therefore aims to discover factors affecting road freight overloading based on highway FW data, with a view of developing strategies to mitigate such occurrences. A comprehensive sampling survey of road freight transportation was conducted in Anhui Province (China). Vehicle Characteristics (VC), Operation Mode (OM), and transportation information from a total of 3248 trucks were collected. In order to take advantage of the strengths associated with both statistical modelling techniques and non-parametric methods, a Classification And Regression Tree (CART) technique was integrated with Binary Logistic Regression (BLR) to reveal the factors affecting road freight overloading. The classification efficacy test shows that the BLR–CART method outperformed the BLR method in term of accuracy. It is also revealed that the factors affecting overloading of freight vehicles are the Type of Transportation (ToT), Rated Load (RL), OM, FW during the investigation period, interaction between RL and FW, and interaction among RL, FW, and Average Haul Distance (AHD). Road transport authorities should pay greater attention to these factors in order to improve efficiency and effectiveness of overloading inspection.

scholarly journals Mental State of Central Sterile Supply Department Staff During COVID-19 Epidemic and CART Analysis

2020 ◽  
Author(s):  
Wei Pan ◽  
Juan Hu ◽  
Liangying Yi
Keyword(s):  
Mental State ◽  
Factor Score ◽  
Measurement Data ◽  
Regression Tree ◽  
General Information ◽  
Classification And Regression Tree ◽  
Psychological Resilience ◽  
Chi Square ◽  
Factors Affecting ◽  
Cart Analysis

Abstract Background: During COVID-19 epidemic, the central sterile supply department (CSSD) staff need to handle a large number of devices, utensils and non-disposable protective articles used by suspected or confirmed COVID-19 patients. This may bring psychological stress to the CSSD staff. However, the mental state of the CSSD staff during COVID-19 epidemic has been rarely studied. We aim to investigate the mental state of the CSSD staff and the relevant influencing factors during COVID-19 epidemic.Methods: Conduct the questionnaire survey with the general information questionnaire, Chinese perceived stress scale (CPSS), self-rating anxiety scale (SAS), and Connor-Davidson resilience scale (CD-RISC) among 423 CSSD staff from 35 hospitals in Sichuan Province, China. Analyse the data in SPSS 24.0, use classification and regression tree (CART) to analyse variables, and find variation between groups. Perform chi-square test on enumeration data, and perform t-test and analysis of variance on measurement data.Results: The CSSD staff’s SAS score was 37.39 ± 8.458, their CPSS score was 19.21 ± 7.265, and their CD-RISC score was 64.26 ± 15.129 (Tenacity factor score: 31.70 ± 8.066, Strength factor score: 21.60 ± 5.066, Optimism factor scores: 10.96 ± 3.189). The CPSS score was positively correlated with the SAS score (r = 0.66; P < 0.01), the CPSS score was negatively correlated with the CD-RISC score (r = -0.617, P < 0.01), and the SAS score was negatively correlated with the CD-RISC score (r = -0.477, P < 0.01). The job, age and political status of the CSSD staff were the main factors affecting their mental state. The CPSS score and SAS score of the CSSD nurses were significantly different from those of the CSSD logistic staff (P < 0.01). Conclusion: During the epidemic, the CSSD staff’s psychological resilience was at a low level, and the anxiety level of the CSSD nurses was higher than that of the CSSD logistic staff. Therefore, more attention shall be paid to the mental health of the CSSD staff, and it is necessary to take the protective measures regarding the risk factors at work to ensure they can maintain a good mental state during the epidemic.

scholarly journals Detection of Subclinical Keratoconus Using a Novel Combined Tomographic and Biomechanical Model Based on an Automated Decision Tree

2021 ◽  
Author(s):  
Peng Song ◽  
Shengwei Ren ◽  
Yu Liu ◽  
Pei Li ◽  
Qingyan Zeng
Keyword(s):  
Decision Tree ◽  
Amplitude Ratio ◽  
Regression Tree ◽  
Biomechanical Model ◽  
Study Data ◽  
Classification And Regression Tree ◽  
Stiffness Parameter ◽  
Chi Square ◽  
Interaction Detection ◽  
Cart Model

Abstract The aim of this study was to develop a predictive model for subclinical keratoconus (SKC) based on decision tree (DT) algorithms. A total of 194 eyes (including 105 normal eyes and 89 SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. DT models were generated in the training database using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms. The discriminating rules of the CART model selected variables of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in order, while the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. The CART model allowed discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

scholarly journals SME Default Prediction Framework with the Effective Use of External Public Credit Data

2020 ◽  
Vol 12 (18) ◽  
pp. 7575
Author(s):  
Zhichao Luo ◽  
Pingyu Hsu ◽  
Ni Xu
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Financial Data ◽  
Classification And Regression Tree ◽  
Default Prediction ◽  
Public Credit ◽  
The Government ◽  
Credit Data ◽  
Effective Use ◽  
Cart Model

Traditional default prediction models mainly rely on financial data. However, financial data on small and medium-sized enterprises (SMEs) are difficult to obtain, and even when they are available, their opaqueness may hinder analysis. Therefore, traditional prediction models encounter serious problems when being utilized to predict the defaulting of SMEs. In this paper, a novel prediction framework utilizing only external public credit data is proposed. The external public credit data used include SMEs’ basic information (BI), credit information from the government (CIG), and court verdict information (CVI), which can be collected from publicly accessible websites. Records on 15,605 sample companies were collected from approximately 300,000 companies. Among them, 8183 have defaulted. The empirical data were applied to construct prediction models using logistic regression, the classification and regression tree (CART) model, and LightGBM. The best results achieved 0.87 accuracy and 0.92 area under receiver operating characteristic (AUC). The results show that the model only uses the external credit data proven to have significant predict ability, and CIG variables offer the best prediction capacities.

scholarly journals KEY FACTORS AFFECTING RAIL SERVICE QUALITY IN THE NORTHERN ITALY: A DECISION TREE APPROACH

Transport ◽  
2014 ◽  
Vol 29 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Rocío De Oña ◽  
Laura Eboli ◽  
Gabriella Mazzulla
Keyword(s):  
Service Quality ◽  
Regression Tree ◽  
Northern Italy ◽  
Classification And Regression Tree ◽  
Key Factors ◽  
Factors Affecting ◽  
Service Characteristics ◽  
Transit Services ◽  
The City ◽  
Tree Approach

This work concerns with the analysis of transit service quality on the basis of the perceptions directly expressed by the passengers of the services. The transit services supporting the research are offered by rail operators of the Northern Italy, and particularly by regional and suburban lines connecting different towns of the hinterland of the city of Milan, and express lines connecting Milan with the Malpensa airport. The experimental data were collected in a survey conducted in May 2012, and addressed to a sample of more than 16,000 passengers. Passengers expressed their opinions about service characteristics such as safety, cleanliness, comfort, information, personnel. The tool chosen for evaluating service quality is a Classification and Regression Tree Approach (CART), useful for identifying the characteristics mostly influencing the overall service quality. We found that service characteristics like ‘Windows and Doors Working’, ‘Courtesy and Competence on Board’, ‘Information at Stations’, ‘Punctuality of Runs’, ‘Courtesy and Competence in Station’ and ‘Regularity of Runs’ mainly influence service quality.

scholarly journals A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Chunlin Gong ◽  
Liangxian Gu
Keyword(s):  
Learning Algorithm ◽  
Sampling Strategy ◽  
Training Sample ◽  
Classification Performance ◽  
Online Data ◽  
Classification Approach ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Efficient Learning ◽  
Sample Set

In many practical engineering applications, data are usually collected in online pattern. However, if the classes of these data are severely imbalanced, the classification performance will be restricted. In this paper, a novel classification approach is proposed to solve the online data imbalance problem by integrating a fast and efficient learning algorithm, that is, Extreme Learning Machine (ELM), and a typical sampling strategy, that is, the synthetic minority oversampling technique (SMOTE). To reduce the severe imbalance, the granulation division for major-class samples is made according to the samples’ distribution characteristic, and the original samples are replaced by the obtained granule core to prepare a balanced sample set. In online stage, we firstly make granulation division for minor-class and then conduct oversampling using SMOTE in the region around granule core and granule border. Therefore, the training sample set is gradually balanced and the online ELM model is dynamically updated. We also theoretically introduce fuzzy information entropy to prove that the proposed approach has the lower bound of model reliability after undersampling. Numerical experiments are conducted on two different kinds of datasets, and the results demonstrate that the proposed approach outperforms some state-of-the-art methods in terms of the generalization performance and numerical stability.

scholarly journals Occupational Health and Job Satisfaction Assessment of Bus Rapid Transit (BRT) Drivers

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Alireza Samerei ◽  
Kayvan Aghabayk ◽  
Alireza Soltani
Keyword(s):  
Mental Health ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Practical Application ◽  
Bus Drivers ◽  
Factors Affecting ◽  
Urban Bus

Several studies have focused on ergonomics of commercial and urban bus drivers; however, there exists a dearth of research on BRT drivers. This study was conducted to investigate the factors affecting the BRT drivers' mental health and satisfaction. The study was carried out on 171 BRT drivers in Tehran, Iran. The required data were collected through two questionnaires. The Classification and Regression Tree (CART) and Hierarchical clustering (HC) was used to extract factors affecting mental health and satisfaction of BRT drivers. The important factors affecting BRT drivers' mental health were: dispute with passengers, depression, BMI, criminal behaviours of passengers, driver's retirement conditions, driver's family conditions, fatigue and the rostering. In addition, the most important factors affecting driver satisfaction were: bus repairs, driver's seat and the sound inside the cabin. Possible practical application includes: creating a counseling and psychotherapy unit and improving the quality of buses and repairment.

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