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Multistage MR-CART: Multiresponse optimization in a multistage process using a classification and regression tree method

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 159卷 107513-107513页

作者： Lee, Dong-Hee Kim, So-Hee Kim, Kwang-Jae Hanyang Univ Div Ind Informat Studies 222 Wangsimni Ro Seoul 04763 South Korea Pohang Univ Sci & Technol Dept Ind & Management Engn 77 Cheongam Ro Pohang 37673 Gyeongbuk South Korea

A multistage process consists of sequential consecutive stages. In this process, each stage has multiple responses and is affected by its preceding stage, while at the same time, affecting the following stage. This complex structure makes it difficult to optimize the multistage process. Recently, it became easy to obtain a large amount of operational data from the multistage process due to development of information technologies. The proposed method employs a data mining method called a classification and regression tree for analyzing the data and desirability functions for simultaneously optimizing the multiresponse. To consider the relationship between stages, a backward optimization procedure which treats the multiresponse of the preceding stage as the input variables is proposed. The proposed method is described using a steel manufacturing process example and is compared with existing multiresponse optimization methods. The case study shows that the proposed method works well and outperforms the existing methods.

关键词： Multistage process Multiresponse optimization Desirability function Big data Data mining classification and regression tree

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NCART: Neural classification and regression tree for tabular data

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PATTERN RECOGNITION 2024年 154卷

作者： Luo, Jiaqi Xu, Shixin Duke Kunshan Univ Data Sci Res Ctr 8 Duke Ave Kunshan 215000 Jiangsu Peoples R China

Deep learning models have become popular in the analysis of tabular data, as they address the limitations of decision trees and enable valuable applications like semi -supervised learning, online learning, and transfer learning. However, these deep -learning approaches often encounter a trade-off. On one hand, they can be computationally demanding when dealing with large-scale or high -dimensional datasets. On the other hand, they may lack interpretability and may not be suitable for small-scale datasets. In this study, we propose a novel interpretable neural network called Neural classification and regression tree (NCART) to overcome these challenges. NCART is a modified version of Residual Networks that replaces fully -connected layers with multiple differentiable oblivious decision trees. By integrating decision trees into the architecture, NCART maintains its interpretability while benefiting from the end -to -end capabilities of neural networks. The simplicity of the NCART architecture makes it well -suited for datasets of varying sizes and reduces computational costs compared to state-of-the-art deep learning models. Extensive numerical experiments demonstrate the superior performance of NCART compared to existing deep learning models, establishing it as a strong competitor to tree -based models. The code is available at https://***/Luojiaqimath/NCART.

关键词： Tabular data Neural networks Interpretability classification and regression tree

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GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2016年第1期188卷 44-44页

作者： Naghibi, Seyed Amir Pourghasemi, Hamid Reza Dixon, Barnali Tarbiat Modares Univ Coll Nat Resources Dept Watershed Management Engn Noor Mazandaran Iran Shiraz Univ Coll Agr Dept Nat Resources & Environm Engn Shiraz Iran Univ S Florida Dept Environm Sci Policy & Geog St Petersburg FL 33701 USA

Groundwater is considered one of the most valuable fresh water resources. The main objective of this study was to produce groundwater spring potential maps in the Koohrang Watershed, Chaharmahal-e-Bakhtiari Province, Iran, using three machine learning models: boosted regression tree (BRT), classification and regression tree (CART), and random forest (RF). Thirteen hydrological-geological-physiographical (HGP) factors that influence locations of springs were considered in this research. These factors include slope degree, slope aspect, altitude, topographic wetness index (TWI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Subsequently, groundwater spring potential was modeled and mapped using CART, RF, and BRT algorithms. The predicted results from the three models were validated using the receiver operating characteristics curve (ROC). From 864 springs identified, 605 (approximate to 70 %) locations were used for the spring potential mapping, while the remaining 259 (approximate to 30 %) springs were used for the model validation. The area under the curve (AUC) for the BRT model was calculated as 0.8103 and for CART and RF the AUC were 0.7870 and 0.7119, respectively. Therefore, it was concluded that the BRT model produced the best prediction results while predicting locations of springs followed by CART and RF models, respectively. Geospatially integrated BRT, CART, and RF methods proved to be useful in generating the spring potential map (SPM) with reasonable accuracy.

关键词： Spring potential mapping Boosted regression tree classification and regression tree Random forest GIS Iran

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Applying classification and regression tree analysis to identify risks of developing sarcopenia in the older population

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INTERNATIONAL JOURNAL OF OLDER PEOPLE NURSING 2022年第6期17卷 e12488页

作者： Nga Thi Thuy Nguyen Huyen Thi Thanh Vu Huey Lan Hu Lin, Kuan Chia Thanh Xuan Nguyen Huang, Hui Chuan Taipei Med Univ Sch Nursing Taipei Taiwan Hanoi Med Univ Fac Nursing & Midwifery Hanoi Vietnam Hanoi Med Univ Dept Geriatr & Gerontol Hanoi Vietnam Natl Geriatr Hosp Sci Res Dept Hanoi Vietnam Natl Yang Ming Chiao Tung Univ Coll Nursing Sch Nursing Taipei Taiwan Natl Yang Ming Chiao Tung Univ Inst Hosp & Hlth Care Management Community Res Ctr Taipei Taiwan Cheng Hsin Gen Hosp Taipei Taiwan

Background and objective: Variations in the risk factors for sarcopenia can lead to differences in the likelihood of developing sarcopenia among older adults;however, few studies have explored the interactions among the risk factors. This study examined the interactions among risk factors and identified a discriminative pathway for groups at risk of sarcopenia in community-dwelling older adults. Methods: A cross-sectional study was conducted between July and August 2019 to recruit 200 older adults from an outpatient department of a hospital providing care for older people. Data on various risk factors, namely demographics (age, gender, education, comorbidities, and body mass index [BMI]), dietary habits (weekly consumption of milk, coffee, and meat), lifestyle behaviours (vitamin D supplementation, smoking, drinking, and physical activity), and depression symptoms were collected. Sarcopenia was defined according to the Asian Working Group for Sarcopenia criteria. A classification and regression tree (CART) model was used to examine interactions among these factors and identify groups at risk of sarcopenia. Findings: The prevalence of sarcopenia was 38.5%. The CART model identified two end groups at differential risks of sarcopenia, with a minimum of one and a maximum of three risk factors. In the first group, low BMI (<18.5 kg/m(2)) was a predominant risk factor for sarcopenia among older people. In the second group, older adults with a normal BMI, aged >= 68 years, and without a regular walking habit had a higher probability of developing sarcopenia than did their counterparts. Conclusions: The interactive effects among older age, BMI, and walking may cause different probabilities of developing sarcopenia in the older population. Implications for practice: Older adults with a low or normal BMI but without a regular walking habit could be a predominant risk group for sarcopenia. The appropriate maintenance of body weight and regular walking activity is suggested to

关键词： body mass index classification and regression tree older age risk factor sarcopenia walking

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Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study

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JOURNAL OF INTENSIVE CARE 2018年第1期6卷 66页

作者： Speiser, Jaime L. Karvellas, Constantine J. Shumilak, Geoffery Sligl, Wendy I. Mirzanejad, Yazdan Gurka, Dave Kumar, Aseem Kumar, Anand Wake Forest Sch Med Dept Biostat Sci Div Publ Hlth Sci Winston Salem NC USA Univ Alberta Dept Crit Care Med 1-40 Zeidler Ledcor Bldg Edmonton AB T6G 2X8 Canada Univ Alberta Div Gastroenterol & Hepatol Edmonton AB Canada Univ Alberta Div Crit Care Med & Infect Dis Edmonton AB Canada Univ Manitoba Sect Crit Care Med Winnipeg MB Canada Univ Manitoba Infect Dis Sect Winnipeg MB Canada Surrey Hosp Surrey BC Canada Rush Med Coll Chicago IL 60612 USA Laurentian Univ Sudbury ON Canada

BackgroundPneumonia complicated by septic shock is associated with significant morbidity and mortality. classification and regression tree methodology is an intuitive method for predicting clinical outcomes using binary splits. We aimed to improve the prediction of in-hospital mortality in patients with pneumonia and septic shock using decision tree *** and regression tree models were applied to all patients with pneumonia-associated septic shock in the international, multicenter Cooperative Antimicrobial Therapy of Septic Shock database between 1996 and 2015. The association between clinical factors (time to appropriate antimicrobial therapy, severity of illness) and in-hospital mortality was evaluated. Accuracy in predicting clinical outcomes, sensitivity, specificity, and area under receiver operating curve of the final model was evaluated in training (n=2111) and testing datasets (n=2111).ResultsThe study cohort contained 4222 patients, and in-hospital mortality was 51%. The mean time from onset of shock to administration of appropriate antimicrobials was significantly higher for patients who died (17.2h) compared to those who survived (5.0h). In the training dataset (n=2111), a tree model using Acute Physiology and Chronic Health Evaluation II Score, lactate, age, and time to appropriate antimicrobial therapy yielded accuracy of 73% and area under the receiver operating curve 0.75. The testing dataset (n=2111) had accuracy of 69% and area under the receiver operating curve *** mortality (51%) in patients with pneumonia complicated by septic shock is high. Increased time to administration of antimicrobial therapy, Acute Physiology and Chronic Health Evaluation II Score, serum lactate, and age were associated with increased in-hospital mortality. classification and regression tree methodology offers a simple prognostic model with good performance in predicting in-hospital mortality.

关键词： Pneumonia Septic shock classification and regression tree Antimicrobial therapy

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An MRI-based decision tree to distinguish lipomas and lipoma variants from well-differentiated liposarcoma of the extremity and superficial trunk: classification and regression tree (CART) analysis

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EUROPEAN JOURNAL OF RADIOLOGY 2020年 127卷 109012-109012页

作者： Shim, Eun Jung Yoon, Min A. Yoo, Hye Jin Chee, Choong Guen Lee, Min Hee Lee, Sang Hoon Chung, Hye Won Shin, Myung Jin Univ Ulsan Asan Med Ctr Coll Med Dept Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea Univ Ulsan Asan Med Ctr Coll Med Res Inst Radiol 88 Olymp Ro 43 Gil Seoul 05505 South Korea Seoul Natl Univ Seoul Natl Univ Hosp Dept Radiol Coll Med 101 Daehak Ro Seoul 03080 South Korea

Purpose: To build and validate a decision tree model using classification and regression tree (CART) analysis to distinguish lipoma and lipoma variants from well-differentiated liposarcoma of the extremities and superficial trunk. Methods: This retrospective study included patients who underwent surgical resection and preoperative contrast-enhanced MR imaging for lipoma, lipoma variants, and well-differentiated liposarcoma in two tertiary referral centers. Six MRI findings (tumor size, anatomical location, tumor depth, shape, enhancement pattern, and presence of intermingled muscle fibers) and two demographic factors (patient age and sex) were assessed to build a classification tree using CART analysis with minimal error cross-validation pruning based on a complexity parameter. Results: The model building cohort consisted of 231 patients (186 lipoma and lipoma variants and 45 well-differentiated liposarcoma) from one center, while the validation cohort consisted of 157 patients (136 lipoma and lipoma variants and 21 well-differentiated liposarcoma) from another center. In the CART analysis, the contrast enhancement pattern (no enhancement or thin septal enhancement versus thick septal, nodular, confluent hazy, or solid enhancement) was the first partitioning predictor, followed by a maximal tumor size of 12.75 cm. The tree model allowed distinction of lipoma and lipoma variants from well-differentiated liposarcoma in both the model building cohort (C-statistics, 0.955;sensitivity 80 %, specificity 94.62 %, accuracy 91.77 %) and the external validation cohort (C-statistics, 0.917;sensitivity 66.67 %, specificity 95.59 %, accuracy 91.72 %). Conclusion: The distinction of lipoma and lipoma variants from well-differentiated liposarcoma can be achieved with the simple classification tree model.

关键词： Lipoma Lipoma variants Well-differentiated liposarcoma MRI classification and regression tree

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Evaluation of ensemble data preprocessing strategy on forensic gasoline classification using untargeted GC-MS data and classification and regression tree (CART) algorithm

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MICROCHEMICAL JOURNAL 2022年 182卷

作者： Ghazi, Md Gezani Bin Md Lee, Loong Chuen Samsudin, Aznor Sheda Binti Sino, Hukil Univ Kebangsaan Malaysia Fac Hlth Sci Forens Sci Program CODTIS Bangi Selangor Malaysia Fire & Rescue Dept Malaysia Fire Invest Div Putrajaya Malaysia Univ Kebangsaan Malaysia Inst IR 4 0 Bangi Selangor Malaysia Fire & Rescue Dept Selangor Fire Invest Div Fire Invest Lab Bangi Malaysia

The purpose of this work is to evaluate the ensemble data preprocessing (DP) strategy composing the selected variant of normalization, parametric time warping and baseline correction techniques in varying sequences for modelling a gas chromatography-mass spectrometry (GC-MS) data via classification and regression tree (CART) algorithm. Firstly, the relative merits between single-DP and ensemble-DP strategies were carefully compared using the best-performing sub-retention time (RT) windows reported elsewhere. Then, all the preprocessed subdatasets were assessed based on predictive capability estimated via the CART algorithm. Performances of CART models were estimated from 50 pairs of training and testing samples that were prepared by a stratified random resampling method. Then, the three shortlisted sub-datasets were further evaluated using increased pairs of training and testing samples. Additionally, the most discriminative RT points were also identified using the three sub-datasets. Eventually, the most desired CART model was constructed using the shortlisted RT points after being treated by the most outstanding DP strategy. Results showed that 3-DP strategies tended to outperform the 1-DP and 2-DP strategies. However, the sequence of application must be carefully optimized as not all the 3-DP strategies induced positive impacts. It was found that the data aligned before baseline correction or normalization will likely outperform those being first normalized or baseline corrected. In conclusion, the untargeted GC-MS data of neat gasoline preferably be first aligned, followed by normalization, and ended by baseline correction.

关键词： Untargeted GC-MS classification and regression tree Gasoline Ensemble data preprocessing technique

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A Study on estimation of demolition waste generation index by using CART(classification and regression tree) analysis

A Study on estimation of demolition waste generation index b...

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2015 International Conference on Structural,Mechanical and Materials Engineering(ICSMME2015)

作者： Gi.Wook Cha Won.Hwa Hong Kang.Guk Lee School of Architectural Civil Environmental and Energy Engineering Kyungpook National University

This study aims to predict amounts of waste generated from detached house by their types and materials when they are constructed and *** achieve this objective, required data was collected based on material information of the buildings before *** using the established database, CART analysis was conducted, with the buildings' types and materials as analysis factors, to identify what affects the generation of waste concrete and to estimate indicators of waste *** results were as ***, the most influential factor on generation of waste concrete was types of ***, generation of waste concrete of RC-type and wood-type buildings was not affected by materials, while that of masonry-type buildings was affected by roof ***-type buildings were divided into two categories by roof materials: i) buildings with slab and slab with roof tiles as roof materials and ii) buildings with roof tiles and slate as roof ***, amounts of waste concrete of RC-type and wood-type buildings were 0.324m/m and 0.018 m/m, respectively, and those of masonry-type buildings with roof materials of slab and slab with roof tiles and the other masonrytype buildings were 0.127 m/m and 0.040 m/m, respectively.

关键词： A Study on estimation of demolition waste generation index by using CART analysis classification and regression tree

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A critical appraisal of logistic regression-based nomograms, artificial neural networks, classification and regression-tree models, look-up tables and risk-group stratification models for prostate cancer

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BJU INTERNATIONAL 2007年第4期99卷 794-800页

作者： Chun, Felix K. -H. Karakiewicz, Pierre I. Briganti, Alberto Walz, Jochen Kattan, Michael W. Huland, Hartwig Graefen, Markus Univ Montreal Canc Prognost & Hlth Outcomes Unit Montreal PQ H2X 3J4 Canada Univ Hamburg Dept Urol Hamburg Germany Univ Hamburg Martini Clin Prostate Canc Ctr Hamburg Germany Cleveland Clin Fdn Dept Quantitat Hlth Sci Cleveland OH 44195 USA

OBJECTIVE To evaluate several methods of predicting prostate cancer-related outcomes, i.e. nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group stratification (RGS) models, all of which represent valid alternatives. METHODS We present four direct comparisons, where a nomogram was compared to either an ANN, a look-up table, a CART model or a RGS model. In all comparisons we assessed the predictive accuracy and performance characteristics of both models. RESULTS Nomograms have several advantages over ANN, look-up tables, CART and RGS models, the most fundamental being a higher predictive accuracy and better performance characteristics. CONCLUSION These results suggest that nomograms are more accurate and have better performance characteristics than their alternatives. However, ANN, look-up tables, CART analyses and RGS models all rely on methodologically sound and valid alternatives, which should not be abandoned.

关键词： prediction tools outcome prostate cancer nomogram artificial neural network classification and regression tree look-up table risk group stratification

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Predictors of success of external cephalic version and cephalic presentation at birth among 1253 women with non-cephalic presentation using logistic regression and classification tree analyses

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ACTA OBSTETRICIA ET GYNECOLOGICA SCANDINAVICA 2017年第8期96卷 1012-1020页

作者： Hutton, Eileen K. Simioni, Julia C. Thabane, Lehana McMaster Univ Dept Obstet & Gynecol Hamilton ON Canada McMaster Univ Midwifery Educ Program Hamilton ON Canada McMaster Univ Dept Hlth Res Methods Evidence & Impact HEI Hamilton ON Canada

IntroductionAmong women with a fetus with a non-cephalic presentation, external cephalic version (ECV) has been shown to reduce the rate of breech presentation at birth and cesarean birth. Compared with ECV at term, beginning ECV prior to 37weeks' gestation decreases the number of infants in a non-cephalic presentation at birth. The purpose of this secondary analysis was to investigate factors associated with a successful ECV procedure and to present this in a clinically useful format. Material and methodsData were collected as part of the Early ECV Pilot and Early ECV2 Trials, which randomized 1776 women with a fetus in breech presentation to either early ECV (34-36weeks' gestation) or delayed ECV (at or after 37weeks). The outcome of interest was successful ECV, defined as the fetus being in a cephalic presentation immediately following the procedure, as well as at the time of birth. The importance of several factors in predicting successful ECV was investigated using two statistical methods: logistic regression and classification and regression tree (CART) analyses. ResultsAmong nulliparas, non-engagement of the presenting part and an easily palpable fetal head were independently associated with success. Among multiparas, non-engagement of the presenting part, gestation less than 37weeks and an easily palpable fetal head were found to be independent predictors of success. These findings were consistent with results of the CART analyses. ConclusionsRegardless of parity, descent of the presenting part was the most discriminating factor in predicting successful ECV and cephalic presentation at birth.

关键词： classification and regression tree cesarean section decision tree external cephalic version breech presentation

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