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Detection of Water Hyacinth (Eichhornia crassipes) in Lake Tana, Ethiopia, Using Machine Learning Algorithms

WATER 2023年第5期15卷 880页

作者： Bayable, Getachew Cai, Ji Mekonnen, Mulatie Legesse, Solomon Addisu Ishikawa, Kanako Imamura, Hiroki Kuwahara, Victor S. Soka Univ Grad Sch Sci & Engn Hachioji Tokyo 1928577 Japan Bahir Dar Univ Coll Agr & Environm Sci Bahir Dar 6000 Ethiopia Lake Biwa Environm Res Inst Otsu 5200022 Japan

Lake Tana is Ethiopia's largest lake and is infested with invasive water hyacinth (E. crassipes), which endangers the lake's biodiversity and habitat. Using appropriate remote sensing detection methods and determining the seasonal distribution of the weed is important for decision-making, water resource management, and environmental protection. As the demand for the reliable estimation of E. crassipes mapping from satellite data grows, comparing the performance of different machine learning algorithms could help in identifying the most effective method for E. crassipes detection in the lake. Therefore, this study aimed to examine the ability of random forest (RF), support vector machine (SVM), and classification and regression tree (CART) machine learning algorithms to detect E. crassipes and estimating seasonal spatial coverage of the weed on the Google Earth Engine (GEE) platform using Landsat 8 and Sentinel 2 images. Cloud-masked monthly median composite Landsat 8 and Sentinel 2 data from October 2021 and 2022, January 2022 and 2023, March 2022, and June 2022 were used to represent autumn, winter, spring, and summer, respectively. Four spectral indices were derived and used in combination with spectral bands to improve the E. crassipes detection accuracy. All methods achieved greater than 95% and 90% overall accuracy when using Sentinel 2 and Landsat 8 images, respectively. Using both data sets, all methods achieved a greater than 93% F1 score for E. crassipes detection. Though the difference in performance between the methods was small, the RF was the most accurate, while the SVM and CART methods had the same accuracy. The maximum E. crassipes coverage area was observed in autumn (22.4 km(2)), while the minimum (2.2 km(2)) was observed in summer. Based on Sentinel 2 data, the E. crassipes area coverage decreased significantly by 62.5% from winter to spring and increased significantly by 81.7% from summer to autumn. The findings suggested that the RF classifier w

关键词： classification and regression tree Google Earth Engine random forest support vector machine water hyacinth detection

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NT-Com: A combined machine learning model for picking up first arrival

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COMPUTERS & GEOSCIENCES 2023年 173卷

作者： Chen, Hongfeng Wang, Pengyu Ren, Tao Pan, Zhangrong Liu, Jie Ma, Yanlu China Earthquake Networks Ctr Beijing Peoples R China Northeastern Univ Inst Network Sci & Big Data Technol Software Coll Shenyang Peoples R China Gansu Earthquake Agcy Lanzhou Gansu Peoples R China

We present a combined model named NT-Com that could predict the noise of observation site in China and effectively pick up first arrivals with very high accuracy. Based on a nonlinear autoregressive exogenous model (NARX) known as a variant of recurrent neural network and classification-regression trees (CART), NT-Com picks first arrivals by detecting abrupt points in time series. Several experiments were performed to check the fitting degree and prediction accuracy of NARX on noise signals, and experiments on CART were made to measure the accuracy of first break picking. The experimental results of NT-Com are close to those of experts and better than the traditional automatic detection algorithms. Moreover, the accuracy of NT-Com can reach 96%. The time error of the P-wave first arrival pickup can be shortened to 0.5 s. Because various first arrival patterns are learned during the training process, they can be effectively identified, which leads to a much lower probability of false triggers than traditional automatic detection algorithms. Trained and tested by all seismic data in China Earthquake networks from 2009-2019, the research work finds out a solution for picking up first arrivals by combining a simple structure of the neural networks and trees, which can be applied in China with its strong generalization ability and robustness.

关键词： P-wave first arrival Machine learning Nonlinear auto-regressive exogenous model classification and regression tree

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Predicting pre- and post-operative acute kidney injury in elderly patients with coronary artery disease

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HELIYON 2024年第13期10卷 e33988页

作者： Wang, Quanfang Zhang, Yijing Li, Sihan Chen, Jiaojiao Yang, Bo Bai, Chuqi Yang, Luting Qiu, Yulan Wang, Chuhui Dong, Yalin Wang, Taotao Xi An Jiao Tong Univ Affiliated Hosp 1 Dept Pharm Xian 710061 Peoples R China

Background: Limited evidence exists regarding the clinical baseline characteristics at admission for acute kidney injury (AKI) before and after interventional cardiac procedures (ICP) in elderly patients with coronary artery disease (CAD). Methods: A total of 488 elderly patients were enrolled in this retrospective single-center study conducted from January 2019 to July 2022, and a classification and regression tree (CART) analysis was performed to identify the high-risk population. Results: The AKI incidence was 21.1 % (103/488) in this study, with 27 and 76 individuals developing AKI before and after ICP, respectively. CART analysis revealed that exposure to nephrotoxic drugs and diuretics had the strongest predictive capacities for identifying patients at risk of developing pre-ICP AKI, with the incidence among these high-risk patients ranging from 6.5 % to 13.8 %. Meanwhile, the optimum discriminators for identifying those at high risk of postICP AKI were the administration of diuretics, D-value <= -860 mL, age >73 years, and administration of nephrotoxic drugs, and the latter model predicted that the AKI incidence among highrisk patients was between 50.0 % and 60.0 %. Conclusions: Elderly patients with CAD exhibited an elevated incidence of AKI. CART models suggested that exposure to nephrotoxic drugs and diuretics, D-value, and age were significantly associated with AKI in the elderly with CAD. Importantly, these baseline characteristics at admission could be utilized to identify elderly patients at high risk of pre- and post-ICP AKI.

关键词： Elderly patients Acute kidney injury Coronary artery disease Interventional cardiac procedures Clinical baseline characteristics classification and regression tree

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Discriminative Ability of Heel Quantitative Ultrasound in Postmenopausal Women with Prevalent Vertebral Fractures: Application of Optimal Threshold Cutoff Values Using classification and regression tree Models

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CALCIFIED TISSUE INTERNATIONAL 2012年第2期91卷 114-120页

作者： del Carmen Navarro, Maria Saavedra, Pedro Jesus Gomez-de-Tejada, Maria Suarez, Mercedes Hernandez, Diego Sosa, Manuel Univ Las Palmas Gran Canaria Invest Grp Osteoporosis & Bone Mineral Dis Las Palmas Gran Canaria 35005 Canary Islands Spain Univ Las Palmas Gran Canaria Grp Invest Educ & Promot Hlth Las Palmas Gran Canaria 35005 Canary Islands Spain Univ Las Palmas Gran Canaria Dept Math Las Palmas Gran Canaria 35005 Canary Islands Spain Univ Seville Dept Med Seville Spain Hosp Univ Insular Bone Metab Unit Las Palmas Gran Canaria Canary Islands Spain Univ Las Palmas Gran Canaria Grp Invest Osteoporosis & Bone Mineral Dis Las Palmas Gran Canaria 35005 Canary Islands Spain

Quantitative ultrasound (QUS) of the heel has been proposed as a screening tool to evaluate the bone status and risk of osteoporotic fragility fractures. The aim of this study was to define threshold values that would maximize the predictive ability of QUS to discriminate subjects with vertebral fractures using the classification and regression trees (CART) models. A cross-sectional analysis was made of a cohort of 1,132 postmenopausal women with a mean age of 58 years. A total of 205 women (18.1 %) presented with a history of vertebral fracture. For all patients, a questionnaire of osteoporosis risk factors was given and measurements of the heel QUS and bone mineral density at the lumbar spine and the proximal femur, obtained by dual-energy X-ray absorptiometry (DXA), were made. Spinal radiographs were assessed for vertebral fractures. Sensitivity, specificity, predictive values, likelihood ratios, and receiver operator characteristics (ROC) curve QUS values were calculated using the optimal threshold identified in the CART models. Cutoff values calculated from best CART model (i.e., a QUS index > 90.5 %) yielded a sensitivity of 80.3 % (95 % CI 69.2-88.1), a negative predictive value of 94 % (95 % CI 90.1-96.5), and a specificity of 68.8 % (95 % CI 63.3-73.8). This cutoff value would obviate the need to perform DXA in 32.8 % of the women of our population at risk for vertebral fractures. The area under the ROC curve of the best model was 0.8071. QUS was shown to discriminate between women with and without a history of vertebral fracture and constitutes a useful tool for assessing vertebral fracture risk. The application of decision trees (CART analyses) was helpful to define the optimal threshold QUS values.

关键词： classification and regression tree Cutoff Densitometry Vertebral fracture Fracture risk Quantitative ultrasound Threshold Osteoporosis Postmenopausal women

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The Relation Between Clinical Phenotypes, GOLD Groups/Stages and Mortality in COPD Patients - A Prospective Multicenter Study

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INTERNATIONAL JOURNAL OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE 2021年 16卷 1171-1182页

作者： Brat, Kristian Svoboda, Michal Zatloukal, Jaromir Plutinsky, Marek Volakova, Eva Popelkova, Patrice Novotna, Barbora Dvorak, Tomas Koblizek, Vladimir Univ Hosp Brno Dept Resp Dis Jihlavska St 20 Brno 62500 Czech Republic Masaryk Univ Fac Med Jihlavska St 20 Brno 62500 Czech Republic Inst Biostat & Anal Ltd Brno Czech Republic Univ Hosp Olomouc Pulm Dept Olomouc Czech Republic Palacky Univ Fac Med Olomouc Czech Republic Univ Hosp Ostrava Pulm Dept Ostrava Czech Republic Univ Ostrava Fac Med Ostrava Czech Republic Bulovka Hosp Pulm Dept Prague Czech Republic Mlada Boleslav Hosp Pulm Dept Mlada Boleslav Czech Republic Univ Hosp Hradec Kralove Pulm Dept Hradec Kralove Czech Republic Charles Univ Prague Fac Med Hradec Kralove Prague Czech Republic

Introduction: The concept of phenotyping emerged, reflecting specific clinical, pulmonary and extrapulmonary features of each particular chronic obstructive pulmonary disease (COPD) case. Our aim was to analyze prognostic utility of: "Czech" COPD phenotypes and their most frequent combinations, "Spanish" phenotypes and Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages + groups in relation to long-term mortality risk. Methods: Data were extracted from the Czech Multicenter Research Database (CMRD) of COPD. Kaplan-Meier (KM) estimates (at 60 months from inclusion) were used for mortality assessment. Survival rates were calculated for the six elementary "Czech" phenotypes and their most frequent and relevant combinations, "Spanish" phenotypes, GOLD grades and groups. Statistically significant differences were tested by Log Rank test. An analysis of factors underlying mortality risk (the role of confounders) has been assessed with the use of classification and regression tree (CART) analysis. Basic factors showing significant differences between deceased and living patients were entered into the CART model. This showed six different risk groups, the differences in risk were tested by a Log Rank test. Results: The cohort (n=720) was 73.1% men, with a mean age of 66.6 years and mean FEV1 44.4% pred. KM estimates showed bronchiectases/COPD overlap (HR 1.425, p=0.045), frequent exacerbator (HR 1.58, p<0.001), cachexia (HR 2.262, p<0.001) and emphysematous (HR 1.786, p=0.015) phenotypes associated with higher mortality risk. Co-presence of multiple phenotypes in a single patient had additive effect on risk;combination of emphysema, cachexia and frequent exacerbations translated into poorest prognosis (HR 3.075;p<0.001). Of the "Spanish" phenotypes, AE CB and AE non-CB were associated with greater risk of mortality (HR 1.787 and 2.001;both p=0.001). FEV1% pred., cachexia and chronic heart failure in patient history were the major underlying factors determin

关键词： chronic obstructive pulmonary disease COPD phenotypes mortality cluster classification and regression tree CART

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Prediction of Visual Prognosis after Epiretinal Membrane Surgery Using regression tree Analysis

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SEMINARS IN OPHTHALMOLOGY 2021年第8期36卷 665-670页

作者： Sato, Tatsuhiko Shibata, Shunsuke Yamauchi-Mori, Reina Hayashi, Ken Hayashi Eye Hosp Fukuoka Japan

Background and Objective: Confounding factors should be considered when predicting visual prognosis after epiretinal membrane (ERM) surgery. We aimed to predict visual acuity after ERM surgery using regression tree analysis. Patients and Methods: We retrospectively reviewed 343 eyes of 343 patients who underwent vitrectomy for idiopathic ERM. regression tree analysis was performed to predict best-corrected visual acuity (BCVA) at 6 months postoperatively. Results: Patients were first divided into two groups based on preoperative BCVA. Age, central macular thickness, and axial length were associated with visual prognosis in patients with specific preoperative BCVAs. When patients were subdivided into groups 1-8, BCVA was significantly better in group 1 than in groups 4-8 (p <= 0.001), and in groups 2-5 and 7 than in groups 6 and 8 (p <= 0.002). Conclusion: Surgical prognosis in eyes with ERM should be evaluated based on a comprehensive assessment of preoperative characteristics.

关键词： classification and regression tree confounding epiretinal membrane optical coherence tomography vitrectomy

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Analysis of Work-Related Psychological Injury Severity among Construction Trades Workers

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JOURNAL OF MANAGEMENT IN ENGINEERING 2023年第2期39卷 04023001-04023001页

作者： Kamardeen, Imriyas Hasan, Abid Deakin Univ Sch Architecture & Built Environm Geelong Vic 3220 Australia

Poor mental health has become rampant in the construction industry globally, causing productivity and profit losses for organizations as well as disabilities and diminished quality of life for workers. Addressing the challenge is critical for a progressive industry and healthy workforce. Existing literature discusses the causes and effects of poor mental health among construction workers. Yet, it does not, based on real-world incident data, explain vulnerable trades, common incident mechanisms, and recurring psychological injuries, nor does it examine variations of lost time within the incident severity outcome due to psychological injuries. The present study addresses these gaps by analyzing workers' compensation offered to construction trades workers from 2008 to 2019 for psychological injuries in the Australian construction industry. Carpenters and joiners, electricians, plant operators, structural steel workers, and construction and plumbing laborers suffered more permanently incapacitating psychological injuries than other trades. Workers in projects located in metro, small rural towns, and very remote regions were more heavily represented in permanently incapacitating psychological injuries than workers in other locations. The worst psychological injury, which resulted in a combination of permanent incapacity and an extended period of lost time (over 3,000 h), was caused by anxiety combined with depression or stress, or posttraumatic stress disorder from experiencing traumatic incidents, workplace violence and bullying, or vehicle incidents. Age and gender of workers did not show a statistically significant association with psychological injuries. The findings offer practical insights for developing optimized occupational health and safety management programs for improving the mental health of construction workers.

关键词： Construction trades workers Psychological injuries Work stress Mental health Workers' compensation data classification and regression tree Australia

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An Electricity Consumption Disaggregation Method for HVAC Terminal Units in Sub-Metered Buildings Based on CART Algorithm

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BUILDINGS 2023年第4期13卷 967-967页

作者： Yang, Xinyu Ji, Ying Gu, Jiefan Niu, Menghan Beijing Univ Technol Beijing Key Lab Green Built Environm & Energy Effi Beijing 100124 Peoples R China Tongji Univ Coll Architecture & Urban Planning Shanghai 201804 Peoples R China

Obtaining reliable and detailed energy consumption information about building service (BS) systems is an essential prerequisite for identifying energy-saving potential and improving energy efficiency of a building. Therefore, in recent years, energy sub-metering systems have been widely implemented in public buildings in China. A majority of electrical systems and equipment can be directly metered. However, in actual sub-metering systems, the terminal units of heating, ventilation and air conditioning (HVAC) systems, such as fan coils, air handling units and so on, are often mixed with the lighting-plug circuit. This mismatch between theoretical sub-metering systems and actual electricity supply circuits constitutes a lot of challenges in BS system management and control optimization. This study proposed an indirect method to disaggregate the energy consumption of HVAC terminal units from mixed sub-metering data based on the CART algorithm. This method was demonstrated in two buildings in Shanghai. The case study results show that the weighted mean absolute percentage errors (WMAPE) are within 5% and 15% during working hours in the cooling and heating seasons, respectively.

关键词： electricity sub-metering HVAC terminal units hourly energy use classification and regression tree

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Risk factors and the utility of three different kinds of prediction models for postoperative fatigue after gastrointestinal tumor surgery

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SUPPORTIVE CARE IN CANCER 2021年第1期29卷 203-211页

作者： Xu, Xin-Yi Lu, Jin-Ling Xu, Qin Hua, Hong-Xia Xu, Le Chen, Li Nanjing Med Univ Sch Nursing Nanjing Peoples R China Nanjing Med Univ Sch Hlth Policy & Management Nanjing Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Gen Surg Nanjing Peoples R China

Background Postoperative fatigue (POF) is a common complication after gastrointestinal tumor surgery, and it also brings negative effect on prognosis and life quality. However, there are no prediction models for POF, and studies of risk factors are not comprehensive. Therefore, the aim of this study is to investigate the risk factors and pick out the best prediction model for POF and to validate it. Methods A prospective study was conducted for patients undergoing elective gastrointestinal tumor surgery. Physiological, psychological, and socioeconomic factors were collected. Logistic regression, back-propagation artificial neural networks (BP-ANNs), and classification and regression tree (CART) were constructed and compared. Results A total of 598 patients consisting of 463 derivation sample and 135 validation sample were included. The incidence of POF in derivation sample, validation sample, and total were 58.3%, 57.0%, and 58.7%, respectively. Logistic regression results showed age, higher degree of education, lower personal monthly income, advanced cancer, hypoproteinemia, preoperative anxiety or depression, and limited social support were risk factors for POF. Receiver operating characteristic curve (ROC) and performance indices were used to test three models. BP-ANN was the best by the comparison of models, and its strong predictive performance was also validated. Conclusions More attention should be paid on specific patients after gastrointestinal tumor surgery. BP-ANN is a powerful mathematical tool that could predict POF exactly. It may be used as a noninvasive screening tool to guide clinicians for early identification of high-risk patients and grading interventions.

关键词： Gastrointestinal neoplasms Postoperative fatigue Logistic regression Back-propagation artificial neural networks classification and regression tree

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PREDICTION OF RETENTION OF PESTICIDES IN REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY USING classification and regression tree ANALYSIS AND ADAPTIVE NEURO-FUZZY INFERENCE SYSTEMS

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JOURNAL OF LIQUID CHROMATOGRAPHY & RELATED TECHNOLOGIES 2012年第6期35卷 854-865页

作者： Zarei, Kobra Fatemi, Leila Damghan Univ Sch Chem Damghan 36715364 Iran

A quantitative structure-retention relationship (QSRR) was developed to predict the retention of some pesticides in reversed-phase high-performance liquid chromatography with different mobile phase composition. A set of 1497 zero-to three-dimensional descriptors were used for each molecule in the data set. classification and regression tree (CART) was successfully used as a descriptor selection method. In addition to mobile phase composition, two very simple descriptive descriptors (octanol-water partition coefficient and average molecular weight) were also selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). The root mean square errors for the training, validation, and test sets were 0.027, 0.034, and 0.036, respectively. In comparison with artificial neural network method, the results showed CART-ANFIS is a powerful model for prediction of retention of pesticides.

关键词： adaptive neuro-fuzzy inference system classification and regression tree pesticides quantitative structure-retention relationship retention model reversed-phase liquid chromatography

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