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Estimating the Relationship between Landslide Area and Slope Unit Characteristics through classification and regression trees

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Journal of Chinese Soil and Water Conservation 2023年第4期54卷 264-274页

作者： Wu, Chun-Yi Chen, Ya-Xin Chang, Wei-Chun Department of Soil and Water Conservation National Chung Hsing University Taichung Taiwan

Taiwan has unique geographical and geological features. In recent years, climate change has led to the increased frequency of intense rainfall events, leading to sediment disasters on slopelands. In this study, classification and regression tree models, including a landslide area model and a landslide susceptibility model, were established to predict landslide areas in a watershed and to estimate the spatial distribution and scale of landslides. To develop a landslide area predictive model, data were collected on 24 typhoon-triggered landslide inventories in the Shihmen Reservoir watershed and the landslide area of slope units as well as on 12 physiographic factors and 2 rainfall factors. The model was then evaluated using four methods, and a comparison of characteristics was conducted to verify the prediction results. Subsequently, a landslide susceptibility model was established to calculate the landslide susceptibility value of each slope unit and to convert it into a landslide ratio. The predicted landslide area of each slope unit was then combined with the landslide ratio for predicting the landslide area for the entire watershed. The results indicated that among all models, the predicted landslide area model provided the most accurate results, with R² values of 0.989 and 0.500 for the training and testing sets, respectively. These results indicated that the model not only estimated the landslide area of the entire watershed but also provided information on the spatial distribution of landslides. © 2023 Chinese Soil and Water Conservation Society. All rights reserved.

关键词： classification and regression tree landslide area analysis Shihmen Reservoir watershed

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Estimating body weight in Sujiang pigs using artificial neural network, nearest neighbor, and CART algorithms: a comparative study using morphological measurements

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TROPICAL ANIMAL HEALTH AND PRODUCTION 2025年第1期57卷 1-15页

作者： Ergin, Malik Koskan, Ozgur Isparta Univ Appl Sci Fac Agr Dept Anim Sci Isparta Turkiye

The objectives of this study were to evaluate different machine learning algorithms for predicting body weight (BW) in Sujiang pigs using the following morphological traits: age, body length (BL), backfat thickness (BFT), chest circumference (CC), body height (BH), chest width (CW), and hip width (HW). Additionally, this study also investigated which machine learning algorithms could accurately and efficiently predict body weight in pigs using a limited set of morphological traits. For this purpose, morphological measurements of 365 mature (180 +/- 5 days) Sujiang pigs from the Jiangsu Sujiang Pig Breeding Farm in Taizhou, Jiangsu Province, China were used. The age of the pigs (180 +/- 5 days) was also included as a nominal predictor. In total, 218 individual measurements were obtained after data preprocessing. In the Sujiang pig dataset, BW had a significantly positive and high linear relationship with BH, BL, CW, HW, and CC resulting in values of 0.66, 0.72, 0.81, 0.84, and 0.88, respectively (p < 0.01). Artificial neural network (ANN), K-nearest neighbors (KNN), and classification and regression tree (CART) algorithms were used to predict BW. Overall, the ANN algorithm outperformed the other algorithms in this pig dataset according to the goodness of fit criteria of R-2 = 0.85, RMSE = 3.98, MAD = 3.25, MAPE = 4.25, SDR = 0.39, RAE = 0.002, MRAE = 0.008, and AIC = 97.96. The ANN algorithm was trained using several training algorithms, such as the Levenberg-Marquardt algorithm, Bayesian regularization, and scaled conjugate gradient. In addition, the number of neurons in the hidden layer was manipulated to 2, 3, or 4. All training algorithms yielded similar results. However, when the predictor variables were HW, BL, and BH, the Levenberg-Marquardt network had the best ability to predict body weight in Sujiang pigs (R-2 = 0.83). When BH measurements were not included in the model, the model's predictive ability decreased by approximately 5%. According to the results,

关键词： Artificial neural network K nearest neighbor classification and regression tree Sujiang pig Morphological measurements

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Development of clinical screening tool for exocrine pancreatic insufficiency in patients with definite chronic pancreatitis

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PANCREATOLOGY 2024年第4期24卷 545-552页

作者： Othman, Mohamed O. Forsmark, Christopher Yadav, Dhiraj Singh, Vikesh K. Lara, Luis F. Park, Walter Zhang, Zuoyi Yu, Jun Kort, Jens J. Baylor Coll Med 7200 Cambridge StSuite 8B Houston TX 77030 USA Univ Florida Gainesville FL USA Univ Pittsburgh Pittsburgh PA USA Johns Hopkins Univ Baltimore MD USA Ohio State Univ Columbus OH USA Stanford Univ Stanford CA USA AbbVie Inc Data & Stat Sci N Chicago IL USA AbbVie Inc Med Affairs Mettawa IL USA

Background/Objectives: No simple, accurate diagnostic tests exist for exocrine pancreatic insufficiency (EPI), and EPI remains underdiagnosed in chronic pancreatitis (CP). We sought to develop a digital screening tool to assist clinicians to predict EPI in patients with definite CP. Methods: This was a retrospective case -control study of patients with definite CP with/without EPI. Overall, 49 candidate predictor variables were utilized to train a classification and regression tree (CART) model to rank all predictors and select a parsimonious set of predictors for EPI status. Five -fold cross -validation was used to assess generalizability, and the full CART model was compared with 4 additional predictive models. EPI misclassification rate (mRate) served as primary endpoint metric. Results: 274 patients with definite CP from 6 pancreatitis centers across the United States were included, of which 58 % had EPI based on predetermined criteria. The optimal CART decision tree included 10 variables. The mRate without/with 5 -fold cross -validation of the CART was 0.153 (training error) and 0.314 (prediction error), and the area under the receiver operating characteristic curve was 0.889 and 0.682, respectively. Sensitivity and specificity without/with 5 -fold cross -validation was 0.888/0.789 and 0.794/0.535, respectively. A trained second CART without pancreas imaging variables (n 1 / 4 6), yielded 8 variables. Training error/prediction error was 0.190/0.351;sensitivity was 0.869/0.650, and specificity was 0.728/0.649, each without/with 5 -fold cross -validation. Conclusion: We developed two CART models that were integrated into one digital screening tool to assess for EPI in patients with definite CP and with two to six input variables needed for predicting EPI status. Published by Elsevier B.V. on behalf of IAP and EPC. This is an open access article under the CC BY -NC -ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Chronic pancreatitis classification and regression tree EPI Predictive modeling

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Machine Learning-Based classification and regression Approach for Early Detection of Large Break Loss-of-Coolant Accident Conditions

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-21页

作者： Almomani, Belal Khuwaileh, Bassam A. Alam, Syed Bahauddin King Fahd Univ Petr & Minerals KFUPM Interdisciplinary Res Ctr Ind Nucl Energy IRC INE Dept Mech Engn Dhahran 31261 Saudi Arabia Univ Sharjah Dept Mech & Nucl Engn Sharjah 27272 U Arab Emirates Univ Illinois Talbot Lab Nucl Plasma & Radiol Engn MC-234104 South Wright StSuite 100 Urbana IL 61801 USA Univ Illinois Natl Ctr Supercomp Applicat 1205 W Clark St Urbana IL 61801 USA

Data-driven predictive approaches have significant potential for enhancing accident management and operator support in nuclear power plants (NPPs). Machine learning (ML)-based models can effectively monitor early signs of accidents and define initial conditions, allowing for better responses and reducing the need for manual intervention. This study explores using ML algorithms to identify the simulation-based initial conditions of accidents in a generic pressurized water reactor (GPWR), focusing on large loss-of-coolant accidents (LLOCAs) in primary cooling systems. Fuel temperature datasets were generated from the GPWR simulator for different LLOCA conditions, including 6 locations and break sizes of 0.11 m(2) to 0.22 m(2). The dataset includes 713,196 observations of fuel temperature from 12 zones of the reactor vessel for 36 LLOCA conditions within a 30-min interval. The classification and regression tree (CART) models were then used to predict the location and size of the break. Following post-learning and validation, CART model performed reasonably well with R-2 value of similar to 0.9 for break size prediction and a misclassification error of similar to 4% for break location. For performance evaluation, 12 new datasets with varying interpolated break sizes for the 6 locations were introduced, targeting the early detection during the time intervals of up to 10 min. The CART model in this study demonstrated acceptable performance in early identifying the break size and location of LLOCA conditions, with accuracy rates > similar to 70% and relative errors < similar to 6%. This approach aims to enhance emergency management and operator support to ensure timely responses and safe operations in NPPs.

关键词： classification and regression tree Loss of coolant Machine learning Nuclear power plant Pressurized water reactor

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Juvenile Albacore tuna (Thunnus alalunga) foraging ecology varies with environmental conditions in the California Current Large Marine Ecosystem

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FISHERIES OCEANOGRAPHY 2023年第5期32卷 431-447页

作者： Nickels, Catherine F. Portner, Elan J. Snodgrass, Owyn Muhling, Barbara Dewar, Heidi NOAA Fisheries Resources Div Southwest Fisheries Sci Ctr Natl Marine Fisheries Serv 8901 La Jolla Shores Dr La Jolla CA 92037 USA Univ Calif San Diego Scripps Inst Oceanog 9500 Gilman Dr La Jolla CA 92093 USA Univ Calif Santa Cruz Inst Marine Sci 1156 High St Santa Cruz CA 95064 USA

Juvenile North Pacific Albacore tuna (Thunnus alalunga) support commercial and recreational fisheries in the California Current Large Marine Ecosystem (CCLME), where they forage during summer and fall. The distributions of the commercial and recreational fisheries and estimates of forage availability have varied substantially over the past century. Time-series quantifying Albacore diet can help link forage composition to variability in Albacore abundance and distribution and, consequently, their availability to fishers. Previous diet studies in the CCLME are of relatively short duration, and long-term variability in Albacore diet remains poorly understood. We describe the diets of juvenile Albacore from three regions in the CCLME from 2007 to 2019 and use classification and regression tree analysis to explore environmental drivers of variability. Important prey include Northern Anchovy (Engraulis mordax), rockfishes (Sebastes spp.), Boreal Clubhook Squid (Onychoteuthis borealijaponica), euphausiids (Order: Euphausiidae), and amphipods (Order: Amphipoda), each contributing >5% mean proportional abundance. Most prey items were short lived species or young-of-the-year smaller than 10 cm. Diet variability was related to environmental conditions over the first 6 months of the year (PDO, sea surface temperature, and NPGO) and conditions concurrent with Albacore capture (region and surface nitrate flux). We describe foraging flexibility over regional and annual scales associated with these environmental influences. Continuous, long-term studies offer the opportunity to identify flexibility in Albacore foraging behavior and begin to make a predictive link between environmental conditions early in the year and Albacore foraging during summer and fall.

关键词： classification and regression tree diet environmental drivers fisheries interactions foraging strategy prey stomach contents

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Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine

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EUROPEAN JOURNAL OF NUTRITION 2024年第5期63卷 1635-1649页

作者： Qasrawi, Radwan Badrasawi, Manal Abu Al-Halawa, Diala Polo, Stephanny Vicuna Abu Khader, Rami Al-Taweel, Haneen Abu Alwafa, Reem Zahdeh, Rana Hahn, Andreas Schuchardt, Jan Philipp Al Quds Univ Dept Comp Sci Jerusalem Palestine Istinye Univ Dept Comp Engn Istanbul Turkiye An Najah Natl Univ Fac Agr & Vet Med Dept Nutr & Food Technol Nablus West Bank Palestine Palestine Polytech Univ Coll Appl Sci Dept Appl Chem & Biol Hebron West Bank Palestine Leibniz Univ Hannover Inst Food Sci & Human Nutr Hannover Germany

PurposeThis study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university *** employed K-means clustering analysis algorithm and Decision tree (DT) technique to identify the association between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters for improving ML models' accuracy. In addition, t-tests and Analysis of Variance (ANOVA) were performed to identify significant differences between the clusters. We evaluated the models on a balanced dataset of 755 female participants from the Hebron district in *** study found that 34.8% of the participants were anemic. The intake of various micronutrients (i.e., folate, Vit A, B5, B6, B12, C, E, Ca, Fe, and Mg) was below RDA/AI values, which indicated an overall unbalanced malnutrition in the present cohort. Anemia was significantly associated with intakes of energy, protein, fat, Vit B1, B5, B6, C, Mg, Cu and Zn. On the other hand, intakes of protein, Vit B2, B5, B6, C, E, choline, folate, phosphorus, Mn and Zn were significantly lower in anemic than in non-anemic subjects. DT classification models for vitamins and minerals (accuracy rate: 82.1%) identified an inverse association between intakes of Vit B2, B3, B5, B6, B12, E, folate, Zn, Mg, Fe and Mn and prevalence of *** the nutrients commonly known to be linked to anemia-like folate, Vit B6, C, B12, or Fe-the cluster analyses in the present cohort of young female university students have also found choline, Vit E, B2, Zn, Mg, Mn, and phosphorus as additional nutrients that might relate to the development of anemia. Further research is needed to elucidate if the intake of these nutrients might influence the risk of anemia.

关键词： Iron deficiency anemia Nutrient intake Dietary patterns classification and regression tree Machine learning K-means analysis

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Development of a clinical prediction rule for mobility status at discharge in patients with total knee arthroplasty: Using a decision tree model

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JOURNAL OF ORTHOPAEDIC SCIENCE 2024年第6期29卷 1451-1455页

作者： Kuwahara, Kenta Kato, Toshihiro Akatsuka, Yuko Nakazora, Shigeto Fukuda, Aki Asada, Keiji Suzuka Univ Med Sci Grad Sch Hlth Sci Div Hlth Sci Suzuka Japan Suzuka Kaisei Hosp Dept Rehabil Suzuka Japan Mie Univ Hosp Dept Rehabil Tsu Japan Suzuka Kaisei Hosp Dept Orthoped Surg Suzuka Japan

Background: Total knee arthroplasty (TKA) is an effective treatment to improve mobility in patients with severe knee osteoarthritis. However, some patients continue to have poor mobility after surgery. The preoperative identification of patients with poor mobility after TKA allows for better treatment selection and appropriate goal setting. The purpose of this study was to develop a clinical prediction rule (CPR) to predict mobility after TKA. Methods: This study included patients undergoing primary TKA. Predictors of outcome included patient characteristics, physical function, and psychological factors, which were measured preoperatively. The outcome measure was the Timed Up and Go test, which was measured at discharge. Patients with a score of >= 11 s were considered having a low-level of mobility. The classification and regression tree methodology of decision tree analysis was used for developing a CPR. Results: Of the 101 cases (mean age, 72.2 years;71.3 % female), 26 (25.7 %) were classified as low-mobility. Predictors were the modified Gait Efficacy Scale, age, knee pain on the operated side, knee extension range of motion on the non-operated side, and Somatic Focus, a subscale of the Tampa Scale for Kinesiophobia (short version). The model had a sensitivity of 50.0 %, a specificity of 98.7 %, a positive predictive value of 92.9 %, a positive likelihood ratio of 37.5, and an area under the receiver operating characteristic curve of 0.853. Conclusion: We have developed a CPR that, with some accuracy, predicts the mobility outcomes of patients after TKA. This CPR may be useful for predicting postoperative mobility and clinical goal setting.

关键词： Clinical prediction rule Knee osteoarthritis Total knee arthroplasty Timed Up and Go test Mobility classification and regression tree

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Using a decision tree approach to determine hearing aid ownership in older adults

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DISABILITY AND REHABILITATION 2023年第14期45卷 2273-2279页

作者： Tran, Yvonne Tang, Diana McMahon, Catherine Mitchell, Paul Gopinath, Bamini Macquarie Univ Macquarie Univ Hearing Fac Med Hlth & Human Sci Dept Linguist 16 Univ Ave Sydney NSW 2109 Australia Univ Sydney Ctr Vis Res Dept Ophthalmol Sydney NSW Australia Univ Sydney Westmead Inst Med Res Sydney NSW Australia

Purpose The main clinical intervention for older adults with hearing loss is the provision of hearing aids. However, uptake and usage in this population have historically been reported as low. The aim of this study was to understand the hearing loss characteristics, from measured audiometric hearing loss and self-perceived hearing handicap, that contribute to the decision of hearing aid ownership. Materials and methods A total of 2833 adults aged 50+ years, of which 329 reported hearing aid ownership, were involved with a population-based survey with audiometric hearing assessments. classification and regression tree (CART) analysis was used to classify hearing aid ownership from audiometric measurements and hearing disability outcomes. Results An overall accuracy of 92.5% was found for the performance of the CART analysis in predicting hearing aid ownership from hearing loss characteristics. By including hearing disability, sensitivity for predicting hearing aid ownership increased by up to 40% compared with just audiometric hearing loss measurements alone. Conclusions A decision tree approach that considers both objectively measured hearing loss and self-perceived hearing disability, could facilitate a more tailored and personalised approach for determining hearing aid needs in the older population.

关键词： Hearing loss hearing aids older adults classification and regression tree hearing disability Hearing Handicap Inventory for Elderly Screening

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Comparison of different machine learning models for mass appraisal of real estate

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SURVEY REVIEW 2023年第388期55卷 32-43页

作者： Bilgilioglu, Suleyman Sefa Yilmaz, Haci Murat Aksaray Univ Engn Fac Dept Geomat TR-68100 Aksaray Turkey

The present study aimed to compare five machine learning techniques, namely, artificial neural network (ANN), support vector machine (SVM), chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and random forest (RF) for mass appraisal of real estate. Firstly, 1982 precedent data was collected throughout the entire study area for train and test models. Secondly, a total of 68 variables were considered for the mass appraisal. Subsequently, the five machine learning techniques were applied. Finally, the receiver operating characteristic (ROC) and various statistical methods were applied to compare five machine learning techniques.

关键词： Machine learning Mass appraisal Artificial neural network Support vector machine Chi-square automatic interaction detection classification and regression tree Random forest

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_Combining return to sport, psychological readiness, body mass, hamstring strength symmetry, and hamstring/quadriceps ratio increases the risk of a second anterior cruciate ligament injury

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KNEE SURGERY SPORTS TRAUMATOLOGY ARTHROSCOPY 2023年第11期31卷 5087-5095页

作者： Almeida, Gabriel Peixoto Leao Albano, Thamyla Rocha Rodrigues, Carlos Augusto Silva Tavares, Maria Larissa Azevedo Lima, Pedro Olavo de Paula Univ Fed Ceara Knee & Sports Res Grp Alexandre Barauna St 9491 Andar Rodolfo Teofilo BR-60430160 Fortaleza CE Brazil Univ Fed Ceara Master Program Physiotherapy & Functioning Fortaleza CE Brazil

Purpose To investigate the combinations of variables that comprise the biopsychosocial model domains to identify clinical profiles of risk and protection of second anterior cruciate ligament injury. Methods One hundred and forty-five patients for return-to-sport testing after anterior cruciate ligament (ACL) reconstruction (ACLR) were contacted, and 97 were deemed eligible. All were evaluated between 6 and 24 months and followed up for 2 years. Participants answered the International Knee Documentation Committee (IKDC) and Anterior Cruciate Ligament-Return to Sport after Injury Scale (ACL- RSI), performed the postural stability assessment using the Biodex Balance System, and assessed muscle strength at 60 degrees and 300 degrees/s on the isokinetic dynamometer. Personal factors (age, gender, body mass index), body structures (graft type and concomitant injuries), and environmental factors (time between surgery and evaluation) were also collected. The participants were asked about the occurrence of a second ACL injury and return to sport after 2 years of follow-up. classification and regression tree (CART) analysis was used to determine predictors of a second ACL injury. The receiver operating characteristic (ROC) curve was performed to verify the accuracy of the CART analysis, in addition to the sensitivity, specificity, and relative risk (RR) of the model. Results Of the initial 97 participants, 88 (89.8%) responded to follow- up and 14 (15.9%) had a second ACL injury (11 graft ruptures and three contralateral ACL). CART analysis identified the following variables as predictors of second ACL injury: return to sport, hamstring strength symmetry at 300 degrees/s, ACL-RSI score, hamstrings/quadriceps ratio at 60 degrees/s, and body mass index (BMI). CART correctly identified 9 (64.3%) of the 14 participants who were reinjured and 71 (95.9%) of the 74 participants who were not. The total correct classification was 90.9%. The area under the ROC curve was 0.88 (95% CI 0.

关键词： Return to sport classification and regression tree Knee

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