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Dissecting Alzheimer's Disease Risk in Asian American Elders: A classification and regression tree Approach

JOURNAL OF ALZHEIMERS DISEASE REPORTS 2024年第1期8卷 517-530页

作者： Moon, Sung Seek Anderson, Lindsey Li, Jinyu Lee, Jinwon Moon, Youngkwang Baylor Univ Diana R Garland Sch Social Work 811 Washington Ave Waco TX 76701 USA Creat Solut Healthcare Dallas TX USA

Background: Alzheimer's disease (AD) poses a growing public health challenge, particularly with an aging population. While extensive research has explored the relationships between AD, socio-demographic factors, and cardiovascular risk factors, a notable gap exists in understanding these connections within the Asian American elderly population. Objective: This study aims to address this gap by employing the classification and regressiontree (CART) approach to investigate the intricate interplay of socio-demographic variables, cardiovascular risk factors, sleep patterns, prior antidepressant use, and AD among Asian American elders. Methods: Data from the 2017 Uniform Data Set, provided by the National Alzheimer's Coordinating Center, were analyzed, focusing on a sample of Asian American elders (n = 4,343). The analysis utilized the classification and regression tree (CART) approach. Results: CART analysis identified critical factors, including levels of independence, specific age thresholds (73.5 and 84.5 years), apnea, antidepressant use, and body mass index, as significantly associated with AD risk. Conclusions: These findings have far-reaching implications for future research, particularly in examining the roles of gender, cultural nuances, socio-demographic factors, and cardiovascular risk elements in AD within the Asian American elderly population. Such insights can inform tailored interventions, improved healthcare access, and culturally sensitive policies to address the complex challenges posed by AD in this community.

关键词： Alzheimer's disease Asian American classification and regression tree National Alzheimer's Coordinating Center

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A Fast Stability Assessment Scheme based on classification and regression tree

A Fast Stability Assessment Scheme based on Classification a...

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IEEE International Conference on Power System Technology (POWERCON)

作者： Zheng, Ce Malbasa, Vuk Kezunovic, Mladen Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781467328685

Traditional power system stability analysis based on full model computation shows its drawbacks in real-time applications where fast variations are present at both demand side and supply side. This paper presents the use of Decision trees (DT) for fast evaluation of power system oscillatory stability and voltage stability based on voltage and current phasor measurements. An operating point is grouped into one of several stability categories based on the value of corresponding stability indicator. A new methodology for knowledge base creation has been elaborated to assure practical and sufficient training data sets. Encouraging results are obtained through the performance examination using the generated knowledge base. The impact of DT growing method and node setting on the classification accuracy has been explored. Finally, the differences in performance between regression tree and several other data mining tools have been compared.

关键词： classification and regression tree knowledge base oscillatory stability voltage stability

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Groundwater level prediction of landslide based on classification and regression tree

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Geodesy and Geodynamics 2016年第5期7卷 348-355页

作者： Yannan Zhao Yuan Li Lifen Zhang Qiuliang Wang Key Laboratory of Earthquake Geodesy Institute of Seismology China Earthquake Administration Wuhan Base of Institute of Crustal Dynamics China Earthquake Administration

According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.

关键词： Landslide Groundwater level Prediction classification and regression tree Three Gorges Reservoir area

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Predicting Ozone Layer Concentration Using Multivariate Adaptive regression Splines, Random Forest and classification and regression tree 7th

Predicting Ozone Layer Concentration Using Multivariate Adap...

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7th International Workshop on Soft Computing Applications (SOFA)

作者： Roy, Sanjiban Sekhar Pratyush, Chitransh Barna, Cornel VIT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India Aurel Vlaicu Univ Arad Automat & Appl Informat Arad Romania

ISBN: (纸本)9783319625249

Air pollution is one of the major environmental worries in recent time. Abrupt increase in the concentration of any gas leads to air pollution. The cities are mostly affected due to the abundance of population there. One of the worst gaseous pollutants is OZONE (O3). In this paper, we propose three predictive models for estimation of concentration of ozone gases in the air which are Random Forest, Multivariate Adaptive regression Splines and classification and regression tree. Evaluation of the prediction models indicates that the Multivariate Adaptive regression Splines model describes the dataset better and has achieved significantly better prediction accuracy as compared to the Random Forest and classification and regression tree. A detailed comparative study has been carried out on the performances of Random Forest, Multivariate Adaptive regression Splines and classification and regression tree. MARS gives the result by considering less variables as compared to other two. Moreover, Random Forest takes a little more time for building the tree as the elapsed time was calculated to 45 s in this case. In addition, variable importance for each model has been predicted. Observing all the graphs Multivariate Adaptive regression Splines gives the closest curve of both train and test set when compared. It can be concluded that multivariate adaptive regression splines can be a valuable tool in predicting ozone for future.

关键词： Ozone Multivariable adaptive regression spline Random Forest classification and regression tree

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Improved classification and regression tree based Omni-directional Wheelchair Control with Eye Movement

Improved Classification and Regression Tree based Omni-direc...

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IEEE International Conference on Information and Automation (ICIA)

作者： Yang, Junyou Yang, Guang Bai, Dianchun Liu, Zhicheng Zhou, Long Yang, Deguo Wang, Shuoyu Shenyang Univ Technol Sch Elect Engn Shenyang Liaoning Provin Peoples R China Kochi Univ Technol Sch Syst Engn 185 Miyanokuchi Kami Kochi Japan

ISBN: (纸本)9781509041022

Omni-directional wheelchairs are capable of providing great help for users with difficult walking as wish. In order to help the users with problems controlling wheelchairs with physical contact methods, this paper presents a set of algorithms including improved classification and regression tree (CART), cross validation separation(CVS) and relative angle model (RAM) to control a wheelchair with eye movements. The worldwide researches and some earlier work done about eye movement data processing were introduced which reveal the difficulty of applying eye movement data processing methods into motion control tasks. Afterwards, a kind of improved CART algorithm was proposed to efficiently decrease noisy data and extract useful information from raw data. And CVS along with RAM methods were presented to finish recognition tasks and improve the robustness of the system for nonstandard user generated scanpaths. In the last, an experiment was conducted to verify the effectiveness of the algorithms, and also the future work based on the results was proposed.

关键词： Omni-directional wheelchair classification and regression tree scanpath eye movement

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Assessing the Predictive Utility of Logistic regression, classification and regression tree, Chi-Squared Automatic Interaction Detection, and Neural Network Models in Predicting Inmate Misconduct

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AMERICAN JOURNAL OF CRIMINAL JUSTICE 2015年第1期40卷 47-74页

作者： Ngo, Fawn Govindu, Ramakrishna Agarwal, Anurag Univ South Florida Sarasota Manatee Coll Arts & Sci 8350 N Tmiami Trail Sarasota FL 34243 USA Univ South Florida Sarasota Manatee Coll Business Sarasota FL 34243 USA

This study assesses the relative utility of a traditional regression approach logistic regression (LR) - and three classification techniques - classification and regression tree (CART), chi-squared automatic interaction detection (CHAID), and multi-layer perceptron neural network (MLPNN)-in predicting inmate misconduct. The four models were tested using a sample of inmates held in state and federal prisons and predictors derived from the importation model on inmate adaptation. Multivalidation procedure and multiple evaluation indicators were used to evaluate and report the predictive accuracy. The overall accuracy of the four models varied between 0.60 and 0.66 with an overall AUC range of 0.60-0.70. The LR and MLPNN methods performed significantly better than the CART and CHAID techniques at identifying misbehaving inmates and the CHAID method outperformed the CART approach in classifying defied inmates. The MLPNN method performed significantly better than the LR technique in predicting inmate misconduct among the training samples.

关键词： Actuarial risk assessment techniques Comparative statistical techniques Logistic regression classification and regression tree Chi-squared automatic interaction detection Neural networks Importation model Inmate misconduct

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Incident Duration Model on Urban Freeways Based on classification and regression tree

Incident Duration Model on Urban Freeways Based on Classific...

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2nd International Conference on Intelligent Computation Technology and Automation

作者： Zhao Xiaoqiang Li Ruimin Yu Xinxin Tsinghua Univ Inst Transportat Engn Beijing 100084 Peoples R China

ISBN: (纸本)9780769538044

Effective incident management requires accurate prediction of incident duration. In this paper, classification and regression tree (CART) is employed to model the incident duration. All 65000 incident records from Beijing Transportation Management Bureau are used for model establishment and another 8000 records for validation. The average relative error of the CART model is 29.5197%. It shows that the reliability of the model is quite satisfactory. The average relative error of the prediction on different ring roads of Beijing is approximately the same.

关键词： Decision tree classification and regression tree Incident duration Mutiple linear regression

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Predicting STEM Major Choice: a Machine Learning classification and regression tree Approach

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Journal for STEM Education Research 2023年第2期6卷 358-374页

作者： Chang, Chi-Ning Lin, Shuqiong Kwok, Oi-Man Saw, Guan Kung Virginia Commonwealth University Richmond VA United States American Institutes for Research Austin TX United States Texas A&M University College Station TX United States Claremont Graduate University Claremont CA United States

Despite the increasing demand for professionals in science, technology, engineering, and mathematics (STEM), only a small portion of young people in the USA pursue a postsecondary degree in STEM. To identify the major predictors of STEM participation, this study uses a machine learning approach, a classification and regression tree (CART), to analyze a wide range of individual, family, and school factors obtained from national survey data of US high school freshmen in fall 2009 who eventually enrolled in STEM college majors by 2016. The analytic results indicate that calculus credits, science identity, total STEM credits, and math achievement are the most predictive factors during the high school years of college STEM major selection. The CART-based tree also shows how these four variables interactively predict the likelihood of students enrolling in STEM college majors. © 2023, The Author(s).

关键词： classification and regression tree Course-taking High School Longitudinal Study of 2009–2016 Motivation STEM college major

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A Comparison of Logistic regression, classification and regression tree, and Neural Networks Models in Predicting Violent Re-Offending

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JOURNAL OF QUANTITATIVE CRIMINOLOGY 2011年第4期27卷 547-573页

作者： Liu, Yuan Y. Yang, Min Ramsay, Malcolm Li, Xiao S. Coid, Jeremy W. Sichuan Univ Sch Publ Hlth Dept Hlth Stat Chengdu 610041 Peoples R China Univ Nottingham Sch Community Hlth Sci Div Psychiat Nottingham NG7 2TU England Minist Justice Partnerships & Hlth Strategy Unit London SW1H 9AJ England Queen Mary Univ London Forens Psychiat Res Unit London EC1A 7BE England

Previous studies that have compared logistic regression (LR), classification and regression tree (CART), and neural networks (NNs) models for their predictive validity have shown inconsistent results in demonstrating superiority of any one model. The three models were tested in a prospective sample of 1225 UK male prisoners followed up for a mean of 3.31 years after release. Items in a widely-used risk assessment instrument (the Historical, Clinical, Risk Management-20, or HCR-20) were used as predictors and violent reconvictions as outcome. Multi-validation procedure was used to reduce sampling error in reporting the predictive accuracy. The low base rate was controlled by using different measures in the three models to minimize prediction error and achieve a more balanced classification. Overall accuracy of the three models varied between 0.59 and 0.67, with an overall AUC range of 0.65-0.72. Although the performance of NNs was slightly better than that of LR and CART models, it did not demonstrate a significant improvement.

关键词： Violence reconviction Risk assessment Neural networks classification and regression tree HCR-20

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A Fault Isolation Method via classification and regression tree-Based Variable Ranking for Drum-Type Steam Boiler in Thermal Power Plant

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ENERGIES 2018年第5期11卷 1142页

作者： Yu, Jungwon Jang, Jaeyel Yoo, Jaeyeong Park, June Ho Kim, Sungshin Busan Natl Univ Dept Elect & Comp Engn Busan 46241 South Korea Korea East West Power Co Ltd Tech Solut Ctr Technol & Informat Dept Dangjin 31700 South Korea XEONET Co Ltd CTO Seongnam 13216 South Korea

Accurate detection and isolation of possible faults are indispensable for operating complex industrial processes more safely, effectively, and economically. In this paper, we propose a fault isolation method for steam boilers in thermal power plants via classification and regression tree (CART)-based variable ranking. In the proposed method, binary classification trees are constructed by applying the CART algorithm to a training dataset which is composed of normal and faulty samples for classifier learning then, to perform faulty variable isolation, variable importance values for each input variable are extracted from the constructed trees. The importance values for non-faulty variables are not influenced by faulty variables, because the values are extracted from the trees with decision boundaries only in the original input space;the proposed method does not suffer from smearing effect. Furthermore, the proposed method, based on the nonparametric CART classifier, can be applicable to nonlinear processes. To confirm the effectiveness, the proposed and comparison methods are applied to two benchmark problems and 250 MW drum-type steam boiler. Experimental results show that the proposed method isolates faulty variables more clearly without the smearing effect than the comparison methods.

关键词： drum-type steam boiler fault isolation classification and regression tree variable ranking smearing effect

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