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An Empirical Study on Predicting Blood Pressure Using classification and regression trees

IEEE ACCESS 2018年 6卷 21758-21768页

作者： Zhang, Bing Wei, Zhiyao Ren, Jiadong Cheng, Yongqiang Zheng, Zhangqi Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Univ Hull Sch Engn & Comp Sci Kingston Upon Hull HU6 7RX N Humberside England

Blood pressure (BP) diseases have become one of the major threats to human health. Continuous measurement of BP has proven to be a prerequisite for effective incident prevention. In contrast with the traditional prediction models with low measurement accuracy or long training time, noninvasive BP measurement is a promising use for continuous measurement. Thus in this paper, classification and regression trees (CARTs) are proposed and applied to tackle the problem. First, according to the characteristics of different information, different CART models are constructed. Second, in order to avoid the over-fitting problem of these models, the cross-validation method is used for selecting the optimum parameters so as to achieve the best generalization of these models. Based on the biological data collected from CM400 monitor, this approach has achieved better performance than the common existing models such as linear regression, ridge regression, the support vector machine and neural network in terms of accuracy rate, root mean square error, deviation rate, and Theil inequality coefficient, and the required training time is also comparatively less. With increasing data, the accuracy rate of predicting systolic BP and diastolic BP by CART exceeds 90%, and the training time is less than 0.5 s.

关键词： classification and regression tree pruning blood pressure prediction health monitoring

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QSRR study of psychiatric drugs using classification and regression trees combined with adaptive Neuro-Fuzzy Inference System

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QSAR & COMBINATORIAL SCIENCE 2008年第6期27卷 729-739页

作者： Jalali-Heravi, Mehdi Shahbazikhah, Parvis Ghadiri-Bidhendi, Atieh Sharif Univ Technol Dept Chem Tehran Iran

A new Quantitative Structure-Retention Relationship (QSRR) approach was carried out for prediction of gas-liquid retention times of 124 psychiatric drugs in whole blood on fused-silica capillary column coated with crosslinked methylsilicone with nitrogen-phosphorus detection. After screening the descriptors, a total of 699 topological, geometric, and electronic descriptors (zero- to three-dimensional) representing various structural characteristics were calculated for each molecule in the dataset. Combined method of classification and regression tree (CART) as a feature selection method for the extraction of four relevant descriptors and Adaptive Neuro-Fuzzy Inference System (ANFIS) as a modeling technique was used for the prediction of retention times of diverse set of psychiatric drugs. The Root Mean Square Errors (RMSEs) for the calibration and prediction sets are 0.457 and 0.514, respectively.

关键词： adaptive neuro-fuzzy inference system classification and regression tree gas chromatography psychiatric drugs quantitative structure-retention relationship

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Automated detection of connective tissue by tissue counter analysis and classification and regression trees

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ANALYTICAL CELLULAR PATHOLOGY 2001年第3-4期23卷 153-158页

作者： Smolle, J Kahofer, P Graz Univ Dept Dermatol A-8036 Graz Austria

Objective: To evaluate the feasibility of the CART (classification and regression tree) procedure for the recognition of microscopic structures in tissue counter analysis. Methods: Digital microscopic images of H&E stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements) and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets. Results: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 and r=0.94, respectively;p<0.001). Conclusions: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.

关键词： image analysis malignant melanoma connective tissue classification and regression tree tissue counter analysis

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Distance-based classification and regression trees for the analysis of complex predictors in health and medical research

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STATISTICAL METHODS IN MEDICAL RESEARCH 2021年第9期30卷 2085-2104页

作者： Johns, Hannah Bernhardt, Julie Churilov, Leonid Ctr Res Excellence Stroke Rehabil & Brain Recover Heidelberg Vic Australia Florey Inst Neurosci & Mental Hlth Heidelberg Vic 3084 Australia Univ Melbourne Melbourne Med Sch Parkville Vic Australia

Predicting patient outcomes based on patient characteristics and care processes is a common task in medical research. Such predictive features are often multifaceted and complex, and are usually simplified into one or more scalar variables to facilitate statistical analysis. This process, while necessary, results in a loss of important clinical detail. While this loss may be prevented by using distance-based predictive methods which better represent complex healthcare features, the statistical literature on such methods is limited, and the range of tools facilitating distance-based analysis is substantially smaller than those of other methods. Consequently, medical researchers must choose to either reduce complex predictive features to scalar variables to facilitate analysis, or instead use a limited number of distance-based predictive methods which may not fulfil the needs of the analysis problem at hand. We address this limitation by developing a Distance-Based extension of classification and regression trees (DB-CART) capable of making distance-based predictions of categorical, ordinal and numeric patient outcomes. We also demonstrate how this extension is compatible with other extensions to CART, including a recently published method for predicting care trajectories in chronic disease. We demonstrate DB-CART by using it to expand upon previously published dose-response analysis of stroke rehabilitation data. Our method identified additional detail not captured by the previously published analysis, reinforcing previous conclusions. We also demonstrate how by combining DB-CART with other extensions to CART, the method is capable of making predictions about complex, multifaceted outcome data based on complex, multifaceted predictive features.

关键词： classification and regression tree cart distance stroke

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Prediction of potential seismic damage using classification and regression trees: a case study on earthquake damage databases from Turkey

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NATURAL HAZARDS 2020年第3期103卷 3163-3180页

作者： Yerlikaya-Ozkurt, Fatma Askan, Aysegul Atilim Univ Dept Ind Engn TR-06830 Ankara Turkey Middle East Tech Univ Dept Civil Engn TR-06800 Ankara Turkey

Seismic damage estimation is an important key ingredient of seismic loss modeling, risk mitigation and disaster management. It is a problem involving inherent uncertainties and complexities. Thus, it is important to employ robust approaches which will handle the problem accurately. In this study, classification and regression tree approach is applied on damage data sets collected from reinforced concrete frame buildings after major previous earthquakes in Turkey. Four damage states ranging from None to Severe are used, while five structural parameters are employed as damage identifiers. For validation, results of classification analyses are compared against observed damage states. Results in terms of well-known classification performance measures indicate that when the size of the database is larger, the correct classification rates are higher. Performance measures computed for Test data set indicate similar success to that of Train data set. The approach is found to be effective in classifying randomly selected damage data.

关键词： Earthquakes Seismic damage classification and regression tree Damage prediction

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A comparison of different rule-based statistical models for modeling geogenic groundwater contamination

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ENVIRONMENTAL MODELLING & SOFTWARE 2010年第12期25卷 1650-1657页

作者： Amini, Manouchehr Abbaspour, Karim C. Johnson, C. Annette Eawag Swiss Fed Inst Aquat Sci & Technol CH-8600 Dubendorf Switzerland

There is an increasing interest in modeling groundwater contamination, particularly geogenic contaminant, on a large scale both from the researcher's as well as policy maker's point of view. However, modeling large scale groundwater contamination is very challenging due to the incomplete understanding of geochemical and hydrological processes in the aquifer. Despite the incomplete understanding, existing knowledge provides sufficient hints to develop predictive models of geogenic contamination. In this study we used a global database of fluoride measurements (>60,000 entities), as well as global-scale information relevant to soil, geology, elevation, climate, and hydrology to evaluate several hybrid methods. The hybrid methods were developed by combining two classification techniques including classification and regression tree (CART) and "knowledge based clustering" (KBC) and three predictive techniques including multiple linear regression (MLR), adoptive neuro-fuzzy inference system (ANFIS) and logistic regression (LR). The results indicated that combination of classification techniques and nonlinear predictive method (ANFIS and LR) were more reliable than others and provided a better prediction capability. Among the different hybrid procedures, combination of KBC-ANFIS and also CART-ANFIS resulted in larger true positive rates and smaller false negative rates for both training and test data sets. However, as the CART classifier is very unstable and very sensitive to resampling, the combination of KBC and ANFIS is preferred as it not only is more robust but also is flexible enough to account for geohydrological conditions. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Knowledge based clustering classification and regression tree Adoptive neuro fuzzy inference system Logistic regression Hybrid methods

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Data mining based framework to identify rule based operation strategies for buildings with power metering system

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建筑模拟（英文版） 2019年第2期12卷 195-205页

作者： Shunian Qiu Fan Feng Zhengwei Li Guang Yang Peng Xu Zhenhai Li School of Mechanical Engineering Tongji University Shanghai China

Operation strategies influence the building energy efficiency. In order to enhance the building energy efficiency, it's necessary to adopt proper operation strategies on building equipment. Thus, the identification of existing operation strategies is necessary for the improvement of operation strategies. A data mining (DM) based framework is proposed in this paper to automatical y identify the building operation strategies. The framework includes classification and regression tree (CART), and weighted association rule mining (WARM) method, targeting at three types of rule based control strategies: on/off control, sequencing control (for equipment of the same type), and coordinated control (for equipment of different types). The performance of this framework is validated with power metering system data and manual identification results based on on-site survey of three buildings in Shanghai. The validation results suggest that the proposed framework is capable of identifying building operation strategies accurately and automatical y. Implemented on the original software named BOSA (Building Operation Strategy Analysis), this framework is promising to be used in engineering field to enhance the efficiency of building operation strategy identification work.

关键词： power metering system operation strategy data mining automatic identification weighted association rule mining classification and regression tree

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The relative importance of factors affecting age-specific seedling survival of two co-occurring oak species in southern California

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FOREST ECOLOGY AND MANAGEMENT 2008年第7期255卷 3063-3074页

作者： Tyler, Claudia M. Davis, Frank W. Mahall, Bruce E. Univ Calif Santa Barbara Inst Computat Earth Syst Sci Santa Barbara CA 93106 USA

As has been reported in other oak habitats throughout the Northern Hemisphere, natural recruitment rates of young oaks in California are very low for some species and in some regions. The majority of experimental studies that contribute to our understanding of this oak recruitment pattern in California have been relatively short-term, conducted on a small-scale. Thus, while we have valuable information about the array of factors that are able to limit seedling establishment, we know much less about their relative importance, and how they vary across years, sites, or age classes. To investigate the impacts of factors limiting seedling and sapling establishment of valley oak (Quercus lobata) and coast live oak (Quercus agrifolia) across a landscape and over time we replicated experiments in four different years in Santa Barbara County, California. Experimental factors manipulated included cattle grazing, and access by deer and small mammals. Plots were distributed across three sites with a cumulative area of 200-ha, and seedling survival was recorded for a minimum of 5 years. We used classification and regression tree analysis (CART) to examine the relative importance of factors influencing survival of planted oaks at different life stages. The relative importance of limiting factors varied among age classes for both species. For initial seedling emergence and survival to 6 months planting year was the most important factor and rodent access was the second most important factor for both Q. lobata and Q. agrifolia. For survival of seedlings through their 1st year rodents, planting year, and site were major limiting factors, though their relative importance varied between the two species. For survival from 1.5 to 5 years, Q. lobata was strongly affected by rodents and site, while Q. agrifolia was mainly affected by site, deer browsing (which reduced survival), and indirect effects of winter-spring cattle grazing (which improved survival). Contrary to our expectations, b

关键词： Quercus lobata Quercus agrifolia Mediterranean CART classification and regression tree inter-annual variation seed predation herbivory browsing limiting factors oak recruitment

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Investigating major cause of crashes on Indian expressways and developing strategies for traffic safety management

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INTERNATIONAL JOURNAL OF CRASHWORTHINESS 2023年第5期28卷 581-590页

作者： Khan, Abdul Basit Agrawal, Rajat Jain, S. S. Indian Inst Technol Ctr Transportat Syst Roorkee Uttar Pradesh India Indian Inst Technol Ctr Transportat Syst Dept Management Studies & Associate Fac Roorkee Uttar Pradesh India Indian Inst Technol Ctr Transportat Syst Dept Civil Engn & Associate Fac Transportat Engn Grp Roorkee Uttar Pradesh India

Distraction is one of the most prominent driver characteristics which accounts for several road crashes and is a major cause of concern for almost all road safety institutions. The main objective of this study was to explore and identify various causes of a crash on Yamuna Expressway. Furthermore, another objective includes the detailed study of driver's fault, including in-vehicular distraction and identifying the most and least distracting activity. Moreover, other driver characteristics like fatigue, the reaction of the driver when someone suddenly comes on the way, etc. were also analyzed for their contributions towards crash occurrence. In this study, supervised machine learning techniques viz. classification and regression tree (CART) was used to predict the possibility of crash occurrence. The model results show that using a cell phone in case of adverse weather conditions has the highest likelihood of crash occurrence. Moreover, among other distraction tasks adjustment of radio/compact disc (CD), while driving has the least likelihood of a crash. Further, it was also found that the reaction of the driver when someone comes suddenly on the way (e.g. animal, another crossing vehicle/pedestrian) and long driving hours leading to headache/backache/fatigue also affect the possibility of crash occurrence. Model results were then validated using the testing dataset and it was found that the model is equally efficient in predicting crashes as it was with the training dataset. This study provides insight into several important driver characteristics and will be helpful in developing educational and enforcement strategies thereby reducing crash risk.

关键词： classification and regression tree driver characteristics in-vehicular distraction crash prediction

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A hybrid FAM-CART model for online data classification

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COMPUTATIONAL INTELLIGENCE 2018年第2期34卷 562-581页

作者： Seera, Manjeevan Lim, Chee Peng Tan, Shing Chiang Tunku Abdul Rahman Univ Coll Fac Engn Kuala Lumpur Malaysia Swinburne Univ Technol Fac Engn Comp & Sci Sarawak Campus Kuching Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic Australia Multimedia Univ Fac Informat Sci & Technol Melaka Malaysia

In this paper, an online soft computing model based on an integration between the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) for undertaking data classification problems is presented. Online FAM network is useful for conducting incremental learning with data samples, whereas the CART model prevails in depicting the knowledge learned explicitly in a tree structure. Capitalizing on their respective advantages, the hybrid FAM-CART model is capable of learning incrementally while explaining its predictions with knowledge elicited from data samples. To evaluate the usefulness of FAM-CART, 2 sets of benchmark experiments with a total of 12 problems are used in both offline and online learning modes. The results are examined and compared with those published in the literature. The experimental outcome positively indicates that the online FAM-CART model is useful for tackling data classification tasks. In addition, a decision tree is produced to allow users in understanding the predictions, which is an important property of the hybrid FAM-CART model in supporting decision-making tasks.

关键词： classification and regression tree data classification fuzzy ARTMAP online learning rule extraction

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