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Ensemble learning-based COVID-19 detection by feature boosting in chest X-ray images

IET IMAGE PROCESSING 2020年第16期14卷 4059-4066页

作者： Upadhyay, Kamini Agrawal, Monika Deepak, Desh IIT Delhi Ctr Appl Res Elect New Delhi India Dr Ram Manohar Lohia Hosp Dept Resp Med New Delhi India

The novel coronavirus has spread quite rapidly across the globe. The current testing rate is failing to match the exponential rate of rising cases. Moreover, the available testing methodologies are expensive and time-consuming. A sensitive automated diagnosis is one of the biggest need of the hour. In the proposed work, the authors analyse the chest X-ray images of normal, pneumonia and coronavirus disease-2019 (COVID-19) patients and process them to boost the COVID-specific features (opacities etc.), which enable to perform sensitive identification of COVID-19 patients. The sets of original and processed images are used with a stack of pre-trained deep models for ensemble learning. They used VGG-16 as base-learners, trained with a diverse set of inputs followed by a logistic regression model, the meta learner, to combine the base-learner predictions. The proposed fusion-based model is trained and tested for three types of classification, TYPE-I: binary (NORMAL/ABNORMAL), TYPE-II: binary (PNEUMONIA/COVID-19) and TYPE-III: multi-class (NORMAL/PNEUMONIA/COVID-19). The diagnosis results are quite promising, with high accuracy and sensitivity values for all the cases. The proposed algorithm can be used to assist the medical experts for quick identification and isolation of COVID-19 patients and thereby mitigating the effect of the virus.

关键词： diseases patient diagnosis learning (artificial intelligence) regression analysis diagnostic radiography medical image processing image fusion COVID-19 patients pre-trained deep models base-learners logistic regression model meta learner base-learner predictions fusion-based model sensitivity values ensemble learning-based COVID-19 detection chest X-ray images novel coronavirus current testing rate exponential rate available testing methodologies sensitive automated diagnosis coronavirus disease COVID-specific features

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The impact of marine engine noise exposure on seafarer fatigue: A China case

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OCEAN ENGINEERING 2022年 266卷

作者： Cui, Renda Liu, Zhengjiang Wang, Xinjian Yang, Zaili Fan, Shiqi Shu, Yaqing Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Key Lab Nav Safety Guarantee Liaoning Prov Dalian 116026 Peoples R China Liverpool John Moores Univ Liverpool Logist Offshore & Marine LOOM Res Inst Liverpool L3 3AF England Dalian Maritime Univ Transport Engn Coll Dalian 116026 Peoples R China Wuhan Univ Technol Sch Nav Wuhan 430063 Peoples R China Dalian Maritime Univ Shipping Res Inst Seafarers Res Ctr Dalian 116026 Peoples R China Wuhan Univ Technol Sch Nav Wuhan Peoples R China Dalian Maritime Univ Nav Coll Dalian Peoples R China

Previous relevant studies have revealed that noise and poor sleep quality are two important risk factors causing seafarer fatigue. However, the relationship between marine engine noise and objective sleep parameters has rarely been studied. Using primary data collected from a 28-day on-board experiment and 1 questionnaire survey during both voyage and berthing periods, this study takes a pioneering step to address this crucial relationship. Energy indicators related to the engine noise for 28 days were estimated and 6 objective sleep parameters were used to measure the degree of seafarer fatigue. The findings reveal that as seafarers want to sleep longer to relieve their anxiety and irritability caused by the increased engine noise, the time in bed (TB) and the total sleep time (TST) increased when the engine noise level increased. Meanwhile, with the growing engine noise levels and the higher number of engine noise events, the total wake time after sleep onset (WASO) and the time for sleep onset latency (SOL) increased, and the sleep efficiency (SE) decreased. Energy indicators were significantly associated with objective sleep parameters. Finally, strengthening the content of psychological adjustment in the seafarer training link and cultivating the seafarers' character strength to improve the ability to face harsh environments are recommended. In maritime management, managers should play the role of social work intervention to adjust seafarers' sleep quality and ease fatigue. In the construction of ships, builders should emphatically consider the use of sound insulation materials to reduce noise effect on living areas.

关键词： Maritime safety Seafarer fatigue Engine noise exposure logistic regression model Objective sleep parameters

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Simulation of decadal alpine permafrost distributions in the Qilian Mountains over past 50 years by using logistic regression model

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COLD REGIONS SCIENCE AND TECHNOLOGY 2012年 73卷 32-40页

作者： Zhao, Shangmin Cheng, Weiming Zhou, Chenghu Chen, Xi Chen, Jin Inst Geog Sci & Nat Resources Res CAS State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Xinjiang Inst Ecol & Geog CAS Urumqi 830011 Peoples R China Chinese Acad Sci Grad Univ Beijing 100049 Peoples R China

Based on a bench-mark distribution map of alpine permafrost, topographic factors and meteorological factors in every decade, the decadal distributions of alpine permafrost from 1960 to 2009 in the Qilian Mountains were simulated by using logistic regression model (LM), and the overall accuracy of the simulation results was estimated to above 80%. Moreover, the changing conditions of alpine permafrost in the Qilian Mountains during each adjoining two decades, as well as over the past 50 years, had also been analyzed. The research results show: (1) LM simulation in this research not only decreases the dependency on the field survey data, but achieves dynamic simulation of alpine permafrost distribution;(2) the simulated areas of alpine permafrost in 1960s, 1970s, 1980s, 1990s and 2000s are 10.7 x 10(4) km(2), 9.8 x 10(4) km(2), 10.0 x 10(4) km(2), 8.5 x 10(4) km(2) and 8.7 x 10(4) km(2) respectively. Hence, the distribution of alpine permafrost presents an overall degraded tendency with two slight fluctuations. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Alpine permafrost simulation logistic regression model Qilian Mountains Topographic factors Meteorological factors

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A novel machine learning model and a public online prediction platform for prediction of post-ERCP-cholecystitis (PEC)

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ECLINICALMEDICINE 2022年 48卷 101431-101431页

作者： Zhang, Xu Yue, Ping Zhang, Jinduo Yang, Man Chen, Jinhua Zhang, Bowen Luo, Wei Wang, Mingyuan Da, Zijian Lin, Yanyan Zhou, Wence Zhang, Lei Zhu, Kexiang Ren, Yu Yang, Liping Li, Shuyan Yuan, Jinqiu Meng, Wenbo Leung, Joseph W. Li, Xun Lanzhou Univ Sch Clin Med 1 Lanzhou 730030 Gansu Peoples R China Xuzhou Med Univ Sch Med Informat & Engn Xuzhou 221004 Jiangsu Peoples R China Lanzhou Univ Dept Gen Surg Hosp 1 Lanzhou 730030 Gansu Peoples R China Lanzhou Univ State Key Lab Appl Organ Chem Lanzhou 730030 Gansu Peoples R China Gansu Prov Key Lab Biol Therapy & Regenerat Med T Lanzhou 730030 Gansu Peoples R China Lanzhou Univ Dept Ultrasonog Hosp 1 Lanzhou 730030 Gansu Peoples R China Sun Yat Sen Univ Affiliated Hosp 7 Clin Res Ctr Big Data Ctr Shenzhen 518107 Guangdong Peoples R China UC Davis Med Ctr Div Gastroenterol Sacramento CA 95817 USA Sacramento VA Med Ctr Sacramento CA 95817 USA

Background Endoscopic retrograde cholangiopancreatography (ERCP) is an established treatment for common bile duct (CBD) stones. Post-ERCP cholecystitis (PEC) is a known complication of such procedure and there are no effective models and clinical applicable tools for PEC prediction. Methods A random forest (RF) machine learning model was developed to predict PEC. Eligible patients at The First Hospital of Lanzhou University in China with common bile duct (CBD) stones and gallbladders in-situ were enrolled from 2010 to 2019. logistic regression analysis was used to compare the predictive discrimination and accuracy values based on receiver operation characteristics (ROC) curve and decision and clinical impact curve. The RF model was further validated by another 117 patients. This study was registered with ***, NCT04234126. Findings A total of 1117 patients were enrolled (90 PEC, 8.06%) to build the predictive model for PEC. The RF method identified white blood cell (WBC) count, endoscopic papillary balloon dilatation (EPBD), increase in WBC, residual CBD stones after ERCP, serum amylase levels, and mechanical lithotripsy as the top six predictive factors and has a sensitivity of 0.822, specificity of 0.853 and accuracy of 0.855, with the area under curve (AUC) value of 0.890. A separate logistic regression prediction model was built with sensitivity, specificity, and AUC of 0.811, 0.791, and 0.864, respectively. An additional 117 patients (11 PEC, 9.40%) were used to validate the RF model, with an AUC of 0.889 compared to an AUC of 0.884 with the logistic regression model. Interpretation The results suggest that the proposed RF model based on the top six PEC risk factors could be a promising tool to predict the occurrence of PEC. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Endoscopic retrograde cholangiopancreatography Machine learning Random forest logistic regression model Cholecystitis Complication Risk factors

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Application of Logical regression Function model in Credit Business of Commercial Banks

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第1期7卷 513-522页

作者： Wei, Ying Hasan, Hafnida Nanyang Inst Technol Nanyang Henan Peoples R China Appl Sci Univ Al Eker Bahrain

This paper takes the credit risk management of commercial banks in China as the mainline, and puts forward a quantitative model that is suitable for the credit risk management of commercial banks in China at present - logistic regression model, and takes a commercial bank as an example, using the regression model to conduct empirical research on the credit risk of enterprises. The estimated logistic model was tested with confirmation samples. The results show that when the cut-off point is set to 0.5, the overall correct rate of the model for the credit risk measurement of natural persons and for enterprises reaches 84.9% and 88%, respectively. When the cut-off point is set at 0.7, the overall accuracy is 89.2%. In general, the results of credit risk measurement of bank customers by the logistic model are quite satisfactory. The logistic regression model is easy to understand and efficient, so it is worth popularising and putting into practice in commercial banks in China.

关键词： Credit risk Credit risk of commercial bank logistic regression model

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Predicting Obstructive Coronary Artery Disease in Patients Undergoing Coronary Angiography for Stable Angina

Predicting Obstructive Coronary Artery Disease in Patients U...

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作者： Thomas, Merrill University of Missouri - Kansas City

学位级别：M.S., Master of Science/Master of Surgery

Accurately identifying the pre-test probability of obstructive coronary artery disease (CAD) in patients presenting with chest pain is key in determining the next steps in management. The Diamond-Forrester model, or an updated version, are currently recommended to calculate a patient’s pre-test probability of obstructive CAD in both the American and European guidelines, respectively. However, studies show there continues to be overutilization and inappropriate use of both invasive and non-invasive diagnostic testing in patients presenting with chest pain. Therefore, an updated model to predict the presence of obstructive CAD could help physicians better select patients who may benefit from further testing and avoid expensive testing in those at low risk for whom testing is unlikely to alter their treatment. Using prospectively collected data from patients undergoing coronary angiography for the evaluation of chest pain in the Coronary Angiogram Database of South Australia (CADOSA), we constructed multivariable logistic regression models using patient demographics (age, sex), clinical history (chest pain duration, quality, location, associated symptoms, precipitants, relievers), and co-morbidities (hypertension, dyslipidemia, diabetes, smoking, family history, cerebrovascular disease, peripheral vascular disease) to predict the presence of obstructive CAD. Prior to model development, multiple imputation methods were used to generate 25 randomly imputed datasets. logistic regression models were built on each imputed data separately and the results were pooled to obtain final beta weights. Among 4,004 patients evaluated with coronary angiography for chest pain, just under fifty percent (1988/4004) were found to have obstructive CAD on coronary angiography and the other half (2016/4004) did not. Age, sex, chest pain duration, classic chest pain relievers (rest and nitroglycerin in <5 minutes), classic chest pain precipitants (exercise, emotional stress, cold weather), h

关键词： CAD Risk assessment Pre-test probability Chest pain Cardiovascular disease logistic regression model

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A radiomics signature to identify malignant and benign liver tumors on plain CT images

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020年第4期28卷 683-694页

作者： Yin, Jin Qiu, Jia-Jun Qian, Wei Ji, Lin Yang, Dan Jiang, Jing-Wen Wang, Jun-Ren Lan, Lan Sichuan Univ West China Hosp West China Biomed Big Data Ctr Chengdu Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Univ Texas El Paso Dept Elect & Comp Engn El Paso TX 79968 USA Sichuan Univ West China Hosp Dept Radiol 37 Guoxue Alley Chengdu 610000 Peoples R China

BACKGROUND: In regular examinations, it may be difficult to visually identify benign and malignant liver tumors based on plain computed tomography (CT) images. RCAD (radiomics-based computer-aided diagnosis) has proven to be helpful and provide interpretability in clinical use. OBJECTIVE: This work aims to develop a CT-based radiomics signature and investigate its correlation with malignant/benign liver tumors. METHODS: We retrospectively analyzed 168 patients of hepatocellular carcinoma (malignant) and 117 patients of hepatic hemangioma (benign). Texture features were extracted from plain CT images and used as candidate features. A radiomics signaturewas developed from the candidate features. We performed logistic regression analysis and used a multiple-regression coefficient (termed as R) to assess the correlation between the developed radiomics signature and malignant/benign liver tumors. Finally, we built a logistic regression model to classify benign and malignant liver tumors. RESULTS: Thirteen features were chosen from 1223 candidate features to constitute the radiomics signature. The logistic regression analysis achieved an R = 0.6745, which was much larger than R-alpha = 0.3703 (the critical value of R at significant level alpha = 0.001). The logistic regression model achieved an average AUC of 0.87. CONCLUSIONS: The developed radiomics signature was statistically significantly correlated with malignant/benign liver tumors (p < 0.001). It has potential to help enhance physicians' diagnostic abilities and play an important role in RCADs.

关键词： Radiomics signature texture analysis liver tumor logistic regression model classification between malignant and benign tumors

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Machine learning predictive model based on national data for fatal accidents of construction workers

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AUTOMATION IN CONSTRUCTION 2020年第0期110卷 102974-000页

作者： Choi, Jongko Gu, Bonsung Chin, Sangyoon Lee, Jong-Seok Sungkyunkwan Univ Dept Convergence Future City Suwon 16419 South Korea Sungkyunkwan Univ Dept Ind Engn Suwon 16419 South Korea Sungkyunkwan Univ Sch Civil Architectural Engn & Landscape Architec Suwon 16419 South Korea

The purpose of this study is to develop a prediction model that identifies the potential risk of fatality accidents at construction sites using machine learning based on industrial accident data collected by the Ministry of Employment and Labor (MOEL) of the Republic of Korea from 2011 to 2016. The data details 137,323 injuries and 2846 deaths, and includes age, sex, and length of service of each accident victim, as well as the type of construction, employer scale, and date of the accident. Upon describing the distribution of the dataset, machine learning methods, such as logistic regression, decision tree, random forest, and AdaBoost analyses were applied with the derivation of major variables influencing classification in each algorithm. A comparison of the performance of each model showed the area under the receiver operating characteristic (AUROC) curve to be highest for the random forest method, at 0.9198, which translates to a 91.98% successful predictive rate in terms of classifying workers who could face a high fatality risk. The random forest analysis of this study indicates that the month (season) and employment size are the most influential factors, followed by age, weekday, and service length based on mean decrease Gini values to predict the likelihood of a fatality accident. Moreover, this analysis generated ensemble predictions based on all the factors contained in the dataset. Hence, this study demonstrates the feasibility of machine learning in the construction safety management area. The results obtained can contribute to the prevention of accidents by raising awareness of potential safety risks, by quantitatively predicting fatal accidents and incorporating the findings with a manpower control system at a construction site.

关键词： Safety management Machine learning logistic regression model Decision tree Random forest AdaBoost Fatal accident prediction

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Detection of Ovarian Tumors in Obstetric Ultrasound Imaging Using logistic regression Classifier With an Advanced Machine Learning Approach

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IEEE ACCESS 2020年 8卷 44999-45008页

作者： Zhang, Zheng Han, Yibo Nanyang Inst Technol Sch Software Nanyang 473000 Peoples R China

This paper addresses the use in different stages of pregnancy of ultrasound imaging and to examine the tumors diagnosed during lactation or pregnancy. There are recent advancements in the application of obstetric ultrasound and imaging techniques helpful for improving the outcome of the pregnancy using various Learning techniques. This paper addresses the need to implement sustainable ultrasound standards with an acceptably high maternal and perinatal mortality rates to provide better and more affordable, quality Ultrasonic Flaw (UT) equipment which can improve Obstetric health care. The state-of-the-art learning approach for obstetric ultrasound is a category of methods in machine learning that are gaining popularity and attracting interest in various fields, including image processing and computer vision. In this paper advanced Machine learning processes map a raw input image to the desired output image using logistic regression classifier(LRC) and Convolution neural networks (CNNs) are of particular interest among all Machine learning methods. Furthermore, we have utilized the Internet of Medical Things (IoMT) for obstetric tumor image segmentation and identification of tumors for the medical experts. The experimental results show the LRC based on CNN can be utilized to predict the output of the ultrasound of obstetric with increased maternal and perinatal mobility rates.

关键词： Ultrasonic imaging Tumors Machine learning Pregnancy logistics Biomedical imaging Machine learning convolution neural network logistic regression model obstetric ultrasound IoMT

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Analysis of overwintering indexes of winter wheat in alpine regions and establishment of a cold resistance model

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FIELD CROPS RESEARCH 2022年第0期275卷 108347-108347页

作者： Bao, Yuzhuo Xing, Jinpu Liang, Yi Ren, Zhipeng Fu, Lianshuang Yu, Jing Wang, Duojia Zhang, Da Xu, Qinghua Cang, Jing Northeast Agr Univ Coll Life Sci Harbin 150030 Peoples R China Northeast Agr Univ Coll Agr Harbin 150030 Peoples R China

Wheat is the main food crop in the world. The planting of winter wheat in alpine regions can not only improve the land use efficiency, but also increase the income of farmers. The planting of winter wheat in alpine regions depends on whether it can survive the winter smoothly. Therefore, the study on the physiological process of winter wheat has application *** a three-year field experiment, the relationships between osmotic and anti-oxidative stress in tillering nodes and winter wheat overwintering capacity in alpine regions was examined. A multi-index cold-resistant overwintering capacity model of winter wheat was established using a (machine learning) logistic regression algorithm. The experiment included three winter wheat varieties (Dn1, Dn2, and J22) and eight key physiological indicators of cold resistance. The results showed that in terms of osmotic stress, the water content of tillering node was about 70%, and the Pro content was 2000-2500 mu g g-1, soluble sugar content of 1000 mu g g-1 above is more conducive to winter wheat overwintering;In terms of antioxidant stress ability, all indexes were maintained at a high level, which worked together to maintain the stability of antioxidant system. Through the analysis of receiver operating characteristic (ROC) curve of cold resistance of each index, six indexes of tillering node water content, hardness, pro content, soluble sugar content, MDA content and POD activity were selected to establish the cold resistance model of winter wheat overwintering, and the effect of the model was evaluated by ROC *** winter wheat overwintering model established in this study can provide a convenient and quick method for evaluating the overwintering capacity of novel winter wheat varieties.

关键词： Winter wheat Overwintering Osmotic stress Antioxidant stress logistic regression model

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