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classification and regression trees for epidemiologic research: an air pollution example

ENVIRONMENTAL HEALTH 2014年第1期13卷 1-10页

作者： Gass, Katherine Klein, Mitch Chang, Howard H. Flanders, W. Dana Strickland, Matthew J. Emory Univ Rollins Sch Publ Hlth Dept Epidemiol Atlanta GA 30322 USA Emory Univ Rollins Sch Publ Hlth Dept Environm Hlth Atlanta GA 30322 USA Emory Univ Rollins Sch Publ Hlth Dept Biostat & Bioinformat Atlanta GA 30322 USA

Background: Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures. Methods: We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2-4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a "terminal node". We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model. Results: The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi square statistic of 34.3 (p = 0.001, with 13 degrees of freedom). Conclusions: regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant

关键词： Air pollution CART classification and regression trees Multipollutant Mixtures Pediatric asthma

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classification and regression trees for the evaluation of thyroid cytomorphological characteristics: A study based on liquid based cytology specimens from thyroid fine needle aspirations

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DIAGNOSTIC CYTOPATHOLOGY 2018年第8期46卷 670-681页

作者： Margari, Niki Mastorakis, Emmanouil Pouliakis, Abraham Gouloumi, Alina-Roxani Asimis, Eleftherios Konstantoudakis, Stefanos Ieromonachou, Panayiotis Panayiotides, Ioannis G. Univ Athens Attikon Univ Hosp Dept Cytopathol Athens Greece Venizele Gen Hosp Dept Cytopathol Iraklion Crete Greece Univ Athens Attikon Univ Hosp Dept Pathol 2 Athens Greece Venizele Gen Hosp Dept Pathol Iraklion Crete Greece

BackgroundThis study investigates the potential of classification and regression trees (CARTs) for the evaluation of thyroid lesions. MethodsThe study was performed on 521, histologically confirmed cytological specimens prepared via liquid based cytology. For each specimen, contextual and cellular morphology features were recorded by experienced cytopathologists, as described in everyday cytological practice and The Bethesda System (TBS);these features were subsequently used to construct two CART models, viz. CART-C for the prediction of the cytological diagnosis (according to TBS) and CART-H for the prediction of the histological diagnosis (hereby expressed as either benign or malignant). ResultsCART-C had no statistically significant performance from the cytologists' evaluations and CART-H had a very good predictive performance for the histological status. ConclusionCARTs provide a methodological framework capable for data mining and knowledge extraction. They created simple human understandable rules and classification algorithms that may assist cytopathologists towards decisions based on classification steps, each one linked with a specific risk and moreover by applying cytomorphological characteristics in hierarchical order according to their importance. The two CARTs may be a useful tool for the training of nonexperienced cytopathologists;moreover, they may act as ancillary methods to avoid misdiagnoses and assist quality assurance procedures in the everyday practice of the cytopathology laboratory.

关键词： classification and regression trees liquid based cytology machine learning morphology thyroid cytopathology

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Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study

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LANDSLIDES 2013年第2期10卷 175-189页

作者： Felicisimo, Angel Cuartero, Aurora Remondo, Juan Quiros, Elia Univ Extremadura Univ Ctr Merida Merida 06800 Spain Univ Extremadura Polytech Sch Caceres 10071 Spain Univ Cantabria Fac Sci Santander 39071 Spain

Four statistical techniques for modelling landslide susceptibility were compared: multiple logistic regression (MLR), multivariate adaptive regression splines (MARS), classification and regression trees (CART), and maximum entropy (MAXENT). According to the literature, MARS and MAXENT have never been used in landslide susceptibility modelling, and CART has been used only twice. Twenty independent variables were used as predictors, including lithology as a categorical variable. Two sets of random samples were used, for a total of 90 model replicates (with and without lithology, and with different proportions of positive and negative data). The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) statistic. The main results are (a) the inclusion of lithology improves the model performance;(b) the best AUC values for single models are MLR (0.76), MARS (0.76), CART (0.77), and MAXENT (0.78);(c) a smaller amount of negative data provides better results;(d) the models with the highest prediction capability are obtained with MAXENT and CART;and (e) the combination of different models is a way to evaluate the model reliability. We further discuss some key issues in landslide modelling, including the influence of the various methods that we used, the sample size, and the random replicate procedures.

关键词： Landslide Predictive modelling MLR Multivariate logistic regression MARS Multiple adaptive regression splines CART classification and regression trees MAXENT Maximum entropy

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QSAR studies of a diverse series of antimicrobial agents against Candida albicans by classification and regression trees

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2010年第2期103卷 184-190页

作者： Tan, Shi-Miao Jiao, Jian Zhu, Xiao-Lei Zhou, Yan-Ping Song, Dan-Dan Gong, Hong Yu, Ru-Qin Cent China Normal Univ Coll Chem Minist Educ Key Lab Pesticide & Chem Biol Wuhan 430079 Peoples R China Wuhan Univ Econ & Management Sch Wuhan 430072 Peoples R China Hunan Univ Coll Chem & Chem Engn State Key Lab Chemobiosensing & Chemometr Changsha 410082 Hunan Peoples R China

In the present study, classification and regression trees (CART) was employed for quantitative structure-activity relationships (QSAR) studies of heterogeneous sets of antimicrobial agents against Candida albicans, compared with multiple linear regression (MLR). More than hundred descriptors calculated by Material Studio 4.0 software system were used as the original variables for CART modeling. As to MLR modeling, the splitting variables in CART were taken as the original inputs. Experimental results revealed that the well correlation between the structurally heterogeneous series of antimicrobial agents and the antimicrobial potencies against C. albicans was obtained by CART. In addition, descriptors S_ssO, octupole-yyz, heat of formation, Balaban index-pc octupole-xzz, shadow_Ly, dipole-z, molecular flexibility, shadow_YZ, Zagreb index, quadrupole-yz and density were found to play the most predominant roles in the antimicrobial activities against C. albicans. (C) 2010 Elsevier B.V. All rights reserved.

关键词： QSAR Antimicrobial agents Candida albicans classification and regression trees

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Contrasting regional and national mechanisms for predicting elevated arsenic in private wells across the United States using classification and regression trees

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WATER RESEARCH 2016年 91卷 295-304页

作者： Frederick, Logan VanDerslice, James Taddie, Marissa Malecki, Kristen Gregg, Josh Faust, Nicholas Johnson, William P. Univ Utah Dept Geol & Geophys Salt Lake City UT 84112 USA Univ Utah Sch Med Dept Family & Preventat Med Salt Lake City UT 84112 USA Univ Wisconsin Survey Hlth Wisconsin Madison WI 53706 USA

Arsenic contamination in groundwater is a public health and environmental concern in the United States (U.S.) particularly where monitoring is not required under the Safe Water Drinking Act. Previous studies suggest the influence of regional mechanisms for arsenic mobilization into groundwater;however, no study has examined how influencing parameters change at a continental scale spanning multiple regions. We herein examine covariates for groundwater in the western, central and eastern U.S. regions representing mechanisms associated with arsenic concentrations exceeding the U.S. Environmental Protection Agency maximum contamination level (MCL) of 10 parts per billion (ppb). Statistically significant covariates were identified via classification and regression tree (CART) analysis, and included hydrometeorological and groundwater chemical parameters. The CART analyses were performed at two scales: national and regional;for which three physiographic regions located in the western (Payette Section and the Snake River Plain), central (Osage Plains of the Central Lowlands), and eastern (Embayed Section of the Coastal Plains) U.S. were examined. Validity of each of the three regional CART models was indicated by values >85% for the area under the receiver-operating characteristic curve. Aridity (precipitation minus potential evapotranspiration) was identified as the primary covariate associated with elevated arsenic at the national scale. At the regional scale, aridity and pH were the major covariates in the arid to semi-arid (western) region;whereas dissolved iron (taken to represent chemically reducing conditions) and pH were major covariates in the temperate (eastern) region, although additional important covariates emerged, including elevated phosphate. Analysis in the central U.S. region indicated that elevated arsenic concentrations were driven by a mixture of those observed in the western and eastern regions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Arsenic Groundwater chemistry Mechanism Prediction classification and regression trees

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Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways

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NEURAL COMPUTING & APPLICATIONS 2014年第2期24卷 357-366页

作者： Samadi, Mehrshad Jabbari, Ebrahim Azamathulla, H. Md Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Univ Sains Malaysia River Engn & Urban Drainage Res Ctr REDAC Nibong Tebal 14300 Penang Malaysia

The scour below spillways can endanger the stability of the dams. Hence, determining the scour depth downstream of spillways is of vital importance. Recently, soft computing models and, in particular, artificial neural networks (ANNs) have been used for scour depth prediction. However, ANNs are not as comprehensible and easy to use as empirical formulas for the estimation of scour depth. Therefore, in this study, two decision-tree methods based on model trees and classification and regression trees were employed for the prediction of scour depth downstream of free overfall spillways. The advantage of model trees and classification and regression trees compared to ANNs is that these models are able to provide practical prediction equations. A comparison between the results obtained in the present study and those obtained using empirical formulas is made. The statistical measures indicate that the proposed soft computing approaches outperform empirical formulas. Results of the present study indicated that model trees were more accurate than classification and regression trees for the estimation of scour depth.

关键词： Scour depth Free overfall spillways Model trees classification and regression trees

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Burned area estimations derived from Landsat ETM plus and OLI data: Comparing Genetic Programming with Maximum Likelihood and classification and regression trees

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 142卷 94-105页

作者： Cabral, Ana I. R. Silva, Sara Silva, Pedro C. Vanneschi, Leonardo Vasconcelos, Maria J. Univ Lisbon Sch Agr Forest Res Ctr P-1349017 Lisbon Portugal Univ Lisbon Fac Sci Dept Informat BioISI Biosyst & Integrat Sci Inst P-1749016 Lisbon Portugal Univ Coimbra Dept Informat Engn CISUC P-3030290 Coimbra Portugal Univ Nova Lisboa NOVA IMS P-1070312 Lisbon Portugal

Every year, large areas of savannas and woodlands burn due to natural conditions and land management practices. Given the relevant level of greenhouse gas emissions produced by biomass burning in tropical regions, it is becoming even more important to clearly define historic fire regimes so that prospective emission reduction management strategies can be well informed, and their results Measured, Reported, and Verified (MRV). Thus, developing tools for accurately, and periodically mapping burned areas, based on cost advantageous, expedite, and repeatable rigorous approaches, is important. The main objective of this study is to investigate the potential of novel Genetic Programming (GP) methodologies for classifying burned areas in satellite imagery over savannas and tropical woodlands and to assess if they can improve over the popular and currently applied methods of Maximum Likelihood classification and classification and regression Tree analysis. The tests are performed using three Landsat images from Brazil (South America), Guinea-Bissau (West Africa) and the Democratic Republic of Congo (Central Africa). Burned areas were digitized on-screen to produce mapped information serving as surrogate ground-truth. Validation results show that all methods provide an overestimation of burned area, but GP achieves higher accuracy values in two of the three cases. GP is the most versatile machine learning method available today, but still largely underused in remote sensing. This study shows that standard GP can produce better results than two classical methods, and illustrates its versatility and potential in becoming a mainstream method for more difficult tasks involving the large amounts of newly available data.

关键词： Burned area mapping Genetic Programming Savana woodlands classification and regression trees Maximum Likelihood Landsat ETM plus /OLI

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An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年第PT 2期651卷 2087-2096页

作者： Choubin, Bahram Moradi, Ehsan Golshan, Mohammad Adamowski, Jan Sajedi-Hosseini, Farzaneh Mosavi, Amir Sari Agr Sci & Nat Resources Univ Dept Watershed Management Sari Iran Univ Tehran Dept Reclamat Arid & Mountainous Reg Karaj Iran McGill Univ Dept Bioresource Engn Fac Agr & Environm Sci Macdonald Campus Ste Anne De Bellevue PQ Canada Obuda Univ Inst Automat Kalman Kando Fac Elect Engn Budapest Hungary IASK Inst Adv Studies Koszeg Koszeg Hungary

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Ensemble approach Flood susceptibility Support vector regression Multivariate discriminant analysis classification and regression trees

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Approach to the methodology of classification and regression trees

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GACETA SANITARIA 2008年第1期22卷 65-72页

作者： Trujillano, Javier Sarria-Santamera, Antonio Esquerda, Aurell Badia, Mariona Palma, Matilde March, Jaume Hosp Arnau Vilanova Unidad Cuidados Intensivos Lab Bioquim Lleida 25198 Spain Univ Lleida Dept Ciencias Med Basicas IRBLLEIDA Lleida Spain ISCIII Agny Evaluac Technol Sanitaria Red IRYSS Madrid Spain Univ Alcala Dept Ciencias Sanitarias & Med Sociales Madrid Spain

Objective: To provide an overview of decision trees based on CART (classification and regression trees) methodology. As an example, we developed a CART model intended to estimate the probability of intrahospital death from acute myocardial infarction (AMI). Method: We employed the minimum data set (MIDS) of Andalusia, Catalonia, Madrid and the Basque Country (2001-2002), which included 33,203 patients with a diagnosis of AMI. The 33,203 patients were randomly divided (70% and 30%) into the development (DS;n = 23,277) and the validation (VS;n = 9,926) sets. The CART inductive model was based on Breiman's algorithm, with a sensitivity analysis based on the Gini index and cross-validation. We compared the results with those obtained by using both logistic regression (LR) and artificial neural network (ANN) (multilayer perceptron) models. The developed models were contrasted with the VS and their properties were evaluated with the area under the ROC curve (AUC) (95% confidence interval [CI]). Results: In the DS, the CART showed an AUC = 0.85 (0.86-0.88), LR 0.87 (0.86-0.88) and ANN 0.85 (0.85-0.86). In the VS, the CART showed an AUC = 0.85 (0.85-0.88), LR 0.86 (0.85-0.88) and ANN 0.84 (0.83-0.86). Conclusions: None of the methods tested outperformed the others in terms of discriminative ability. We found that the CART model was much easier to use and interpret, because the decision rules generated could be applied without the need for mathematical calculations.

关键词： classification and regression trees artificial neural networks logistic regression

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Predicting the distribution of invasive crayfish (Pacifastacus leniusculus) in a Kusiro Moor marsh (Japan) using classification and regression trees

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ECOLOGICAL RESEARCH 2006年第2期21卷 271-277页

作者： Usio, N Nakajima, H Kamiyama, R Wakana, I Hiruta, S Takamura, N Natl Inst Environm Studies Biodivers Conservat Res Project Grp Tsukuba Ibaraki 3058506 Japan Yaseiseibutu Sogo Kenkyujyo Sapporo Hokkaido 0010017 Japan Hokkaido Univ Inst Low Temp Sci Sapporo Hokkaido 0600819 Japan Lake Akan Eco Museum Ctr Hokkaido 0850467 Japan Hokkaido Univ Kusiro 0858580 Japan

Summer distributions of the invasive signal crayfish (Pacifastacus leniusculus) were investigated in relation to physicochemistry in a Kusiro Moor marsh and its inflows and outflows in northern Japan. Maximum crayfish abundance and biomass were 1.04 individuals/ m 2 and 3.56 g dry mass (DM)/ m(2) in littoral marsh habitats, and 5.84 individuals/m(2) and 13.48 g DM/m(2) in stream habitats. classification tree analysis was used to predict crayfish occurrence at 102 sites from all habitats (i.e. littoral marsh, pelagic marsh and stream) while regression tree analyses were used to predict crayfish abundance at littoral marsh and stream sites separately. The classification tree showed that crayfish occurrence was primarily determined by undercut bank volume regardless of habitat identity. When undercut bank volume was < 0.0054 m(3), crayfish were predicted to be absent at marsh sites, but expected to occur at stream sites where pH and water temperature exceeded 6.5 and 14.3 degrees C, respectively. The regression tree using only littoral marsh sites showed that undercut bank volume, followed by dissolved oxygen level, determined the splits of the tree. Crayfish abundance was highest when undercut bank volume was > 0.61 m(3), and moderately high when dissolved oxygen was > 9.09 mg/l and undercut bank volume was < 0.61 m(3). On the other hand, the regression tree using only stream sites showed that water temperature was the major predictor that determined the splits. We discuss the roles of physicochemical factors as limiting factors of the distribution pattern of the invasive crayfish.

关键词： signal crayfish exotic species wetland Kushiro Moor classification and regression trees

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