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Very Low Food Security in US Households Is Predicted by Complex Patterns of Health, Economics, and Service Participation

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JOURNAL OF NUTRITION 2017年第10期147卷 1992-2000页

作者： Choi, Seul Ki Fram, Maryah S. Frongillo, Edward A. Univ South Carolina Dept Hlth Promot Educ & Behav Arnold Sch Publ Hlth Columbia SC 29208 USA Univ South Carolina Coll Social Work Columbia SC 29208 USA

Background: Very low food security (VLFS) happens at the intersection of nuanced and complex patterns of risk characteristics across multiple domains. Little is known about the idiosyncratic situations that lead households to experience VLFS. Objective: We used classification and regression tree (CART) analysis, which can handle complex combinations of predictors, to identify patterns of characteristics that distinguish VLFS households in the United States from other households. Methods: Data came from 3 surveys, the 2011-2014 National Health Interview Survey (NHIS), the 2005-2012 NHANES, and the 2002-2012 Current Population Survey (CPS), with sample participants aged >= 18 y and households with income <300% of the federal poverty line. Survey participants were stratified into households with children, adult-only households, and older-adult households (NHIS, CPS) or individuals aged 18-64 y and individuals aged >= 65 y (NHANES). Household food security was measured with the use of the 10-item US Adult Food Security Scale. Variables from multiple domains, including sociodemographic characteristics, health, health care, and participation in social welfare and food assistance programs, were considered as predictors. The 3 data sources were analyzed separately with the use of CART analysis. Results: Household experiences of VLFS were associated with different predictors for different types of households and often occurred at the intersection of multiple characteristics spanning unmet medical needs, poor health, disability, limitation, depressive symptoms, low income, and food assistance program participation. These predictors built complex trees with various combinations in different types of households. Conclusions: This study showed that multiple characteristics across multiple domains distinguished VLFS households. Flexible and nonlinear methods focusing on a wide range of risk characteristics should be used to identify VLFS households and to inform policies and prog

关键词： very low food security household composition classification and regression tree decision tree medical needs food assistance program

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Education and Socio Economic Factors Impact on Earning for Pakistan - A Bigdata Analysis 1st

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1st International Conference on Future Intelligent Vehicular Technologies (Future 5V)

作者： Younas, Neelam Asghar, Zahid Qayyum, Muhammad Khan, Fazlullah Pakistan Inst Dev Econon Islamabad Pakistan Quid e Azam Univ Dept Stat Islamabad Pakistan Abdul Wali Khan Univ Mardan Dept Comp Sci Mardan Pakistan

ISBN: (纸本)9783319512075;9783319512068

This paper give an insight on effect of education and socio economic factors on education on earning for Pakistan using data mining technique regression tree and classification tree (CART). Labor force survey data used in this paper. Variables used as predictors in the study are Education, Gender, Status, Training, and Occupation, Location of working, Training, Experience, Age and Type of industry, where monthly income is used as an independent variable. In case of classification income is divided in Quintiles, which is used as a dependent variable for classification variable. Type of industry, education, age and occupation are found significant variables in both classification and regression tree. regression trees shows that instead of education type of industry is the most important variable and sex and education are the least important variables. classification tree also shows that Type of industry is the most significant variable which effects the earning of an individual, then age and occupation of an individual come and education is the least important variable where the rest of predictors play no role in earning of an individual.

关键词： CART classification and regression tree Pruning Cross validation

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Comparison of Neurocognition and Social Cognition between Schizoaffective Disorder, Mood Disorders, and Schizophrenia

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SOCIAL WORK RESEARCH 2017年第3期41卷 169-179页

作者： Xiao, Rong Bartel, Roxanne L. Brekke, John Univ Utah Dept Psychiat 501 Chipeta Way Salt Lake City UT 84108 USA Univ Southern Calif Suzanne Dworak Peck Sch Social Work Social Work Res Los Angeles CA USA

The relationship between schizoaffective disorder (SA), schizophrenia (SZ), and mood disorders (MD) is not well understood. Evaluating and comparing cognitive impairment in these disorders can help clarify how these disorders are related. The study's 47 participants with SZ, 94 with SA, and 39 with MD were evaluated with the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery. Multivariate analysis of covariance and three Hotelling's T2 tests were used to compare cognition between participants with SZ, SA, and MD. A data mining classification tree determined the order of importance for all the significant predictors. Power analyses confirmed there was adequate statistical power. Results indicated that there was no difference between the performance of participants with SA and MD in all cognitive domains tested. Second, participants with SZ had worse performance than those with SA and MD in the cognitive domains of processing speed, working memory, visual learning, reasoning and problem solving, and social cognition, after controlling for gender, age, years of education, and substance use disorder. Finally, social cognition was the most important variable to distinguish SZ from the other two diagnoses, indicating that SA is more cognitively similar to MD than to SZ.

关键词： classification and regression tree data mining multivariate analysis of covariance severe mental illness

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analysis of the efficacy of the MSHA respirable dust sampling program in surface metal/nonmetal mining industry

analysis of the efficacy of the MSHA respirable dust samplin...

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作者： Lashgari, Ali PennState University Libraries

学位级别：Doctor of Philosophy

The mining industry continues to be an important contributor to the American economy. However, there are a variety of health concerns associated with mining. Exposure to respirable silica dust is one of the sources of health risks in surface mines. A variety of respiratory diseases and other ailments, including silicosis and other non-malignant respiratory diseases, may be caused by exposure to respirable silica dust. The Mine Act of 1977 requires the Mine Safety and Health Administration (MSHA) to inspect mines every year. While one goal of these inspections is to ensure compliance with occupational health and safety regulations, Congress’s overall intent was to improve the safety and health of America’s mine workers. MSHA conducts a vigorous respirable dust sampling program to ensure compliance with its regulations as part of its deterrence and compliance mandate. MSHA inspections are resource intensive and consume a significant percentage of the agency’s annual appropriation. The question of whether MSHA’s compliance-sampling program is accomplishing the intended outcome is one of continued interest to the Government Accountability Office, taxpayers, organized labor, industry, and mine workers. The goal of this research is to determine the efficacy of the MSHA compliance-sampling program for respirable silica dust in surface metal and nonmetal mining operations. It is difficult to evaluate the efficacy of the program in reducing silicosis among mine workers because of the incomplete knowledge of health outcomes among miners and many confounding variables. The consequences of excess silica exposure can take years to develop. It is often impossible to accurately determine where and when exposures occurred, and whether exposures are fully attributable to occupational sources. Therefore, other surrogate metrics are required to assess the extent to which MSHA’s sampling program and enforcement activity is contributing to the intended outcome. Two surrogate measures, i

关键词： Surface Mining Occupational Health and Safety Respirable Dust Silica Compliance Sampling classification and regression tree Metal/Nonmetal Mining Fugitive Dust Dust Exposure Quartz

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Promotion factors for enlisted infantry Marines

Promotion factors for enlisted infantry Marines

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作者： Micah A. Steinpfad Naval Postgraduate School

学位级别：硕士

The Marine Corps dedicates itself to ensuring quality retention and promotion. To accomplish this, we must analyze the effects of policy and the quality of Marines currently serving. This thesis considers data from 97,013 enlisted infantry Marines who entered the Marine Corps from 2001 to 2016 to determine which factors contribute toward promotion for the ranks of lance corporal, corporal, sergeant and staff sergeant. To assess which ranks proportionally promote more high-quality Marines, we compare two performance evaluation methods: proficiency and conduct marks and reporting senior and reviewing officer values. Our analysis of the data shows that the most important factors for promotion are conduct scores, reporting senior relative values, the number of deployments, combat fitness test (CFT) and physical fitness test (PFT) scores and zero adverse fitness reports. From the two performance evaluation methods we find that the Marine Corps promotes proportionally more high-quality Marines as reflected in fitness reports than with proficiency and conduct marks. Promoting and retaining the highest-performing Marines will ensure they are fully prepared to meet current and future challenges to national security. We must develop new methods for retaining more top-quality Marines to ensure their greatest probability of success.

关键词： Marine Corps enlisted promotion infantry predictors performance fitness reports rpart trees classification and regression tree proportion probability quality proficiency conduct reporting senior reviewing officer

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Application of Data Mining Techniques to Quantify the Relative Influence of Design and Installation Characteristics on Labor Productivity

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JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT 2017年第8期143卷 04017052-04017052页

作者： Bonham, Dave R. Goodrum, Paul M. Littlejohn, Ray Albattah, Mohammed A. Turner Construct Co 55 E MonroeSuite 1430 Chicago IL 60603 USA Univ Colorado Construct Engn & Management Dept Civil Environm & Architectural Engn 1111 Engn DrUCB 428 Boulder CO 80309 USA Univ Colorado Management Lockheed Martin Engn Management Program Boulder CO 80309 USA Univ Colorado Dept Civil Environm & Architectural Engn Construct Engn & Management ECCE 156-1601111 Engn DrUCB 428 Boulder CO 80309 USA

The factors affecting productivity have classically been categorized as those related to the work environment and the work to be done, resulting in a piecewise understanding of productivity. In general, the factors among these categories have been considered as influencing the work environment in a mutually exclusive manner. Current industry practices of labor productivity are derived from unitized measures of piping installation under various design parameters. The heterogeneous nature of mechanical piping and plumbing projects introduce a system of installation factors that warrants simplification. This paper presents a methodological approach to develop a practical data collection metric for productivity based on established industry factors of influence. This method is developed to capture the systematic and integrative behaviors of complex piping installation factors in a simple master code structure. Although the methods applied in the paper are used to develop a productivity metric for mechanical piping, the methods could be applied to develop productivity metrics for other systems using relevant data sources. Accordingly, the paper also presents a productivity metric based on the Mechanical Contractors Association of America estimating data sources. A data mining technique utilizing a classification and regression tree (CART) algorithm is used to expose the most influential factors of piping installation on industry recognized standards of estimated labor rates without conceptual bias or industry prejudice. The optimization of progressive CART cases based on three sources of mechanical piping and plumbing estimating data results in post hoc perspectives of productivity factors that are systematically delineated and integrated across their categorical, ordinal, and scalar natures. In each case, the method provides a statistically sound and reproducible result in the form of plausible data collection metric to represent a simple industry-level coding structure

关键词： Construction productivity classification and regression tree Mechanical piping Measurement Labor and personnel issues

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A Comparison of Health Expenditures between Taiwan Families of Different Age Structures

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智慧科技与应用统计学报 2017年第1期15卷 17-30页

作者：林真真徐永豐胡學穎

The data we used for our study consist of family income and expenditure collected in Taiwan between 1997 and 2003. Families are classified into young families if all members are less than 65 years of age, senior families if all members are 65 years of age or older. Families are then further classified into low and high national health insurance (NHI) self-pay expenditure families based on the self-pay amount for national health insurance where the dividing point is the median of the self-pay amount. The main goal of our study is to compare between four types of families: young families with low NHI self-pay expenditure, young families with high NHI self-pay expenditure, senior families with low NHI self-pay expenditure, and senior families with high NHI self-pay expenditure based on classification and regression trees (CART) analyses.

关键词： National Health Insurance Expenditure classification and regression tree

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The Knowledge Discovery of the Nuclear Power Issue Using: the Artificial Intelligence Model: An Example of the CART and the SVM

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JOURNAL OF INTERNET TECHNOLOGY 2016年第6期17卷 1071-1082页

作者： Chung, Yueh-Shiun Ho, Mei Hsiu-Ching Huang, Sun-Jen Liang, Shih-Wu Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan Natl Taiwan Univ Sci & Technol Grad Inst Technol Management Taipei Taiwan Shih Hsin Univ Dept Publ Relat & Advertising Taipei Taiwan

After the nuclear accidents in the Fukushima Daiichi Nuclear Power Plant, the public has become increasingly concerned about nuclear power policies. We employed the CART and the SVM methods to establish and compare classification patterns in order to reveal obscure information in the survey data of Nuclear Power Policy'. Our results showed that the total classification accuracy of the CART is higher than that of SVM or logistic regression. 48.87% of the Taiwanese public opposed nuclear power, 34.16% supported nuclear power, and 16.97% did not respond to the question. However, the CART analysis showed that among the non-respondents, 56% supported nuclear power and 44% opposed its use. The two primary factors influencing the opinion for nuclear power are "satisfaction with the safety of nuclear power" and "improvement in safety inspection and disaster prevention standards in nuclear power plants." Therefore, by improving the safety of nuclear power plant, the government might regain public support for nuclear power.

关键词： Artificial intelligence classification and regression tree Support vector machine Logistic regression Nuclear power issue

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Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

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JOURNAL OF INTELLIGENT MANUFACTURING 2016年第6期27卷 1273-1285页

作者： Seera, Manjeevan Lim, Chee Peng Loo, Chu Kiong Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Deakin Univ Ctr Intelligent Syst Res Geelong Vic Australia

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks.

关键词： classification and regression tree Fault detection and diagnosis Fuzzy min-max neural network Induction motor

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Correlation of radiographic measurements of structures of the equine foot with lesions detected on magnetic resonance imaging

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EQUINE VETERINARY JOURNAL 2016年第2期48卷 165-171页

作者： de Zani, D. Polidori, C. di Giancamillo, M. Zani, D. D. Univ Milan Azienda Polo Vet Lodi Reparto Radiol Lodi Italy

Reasons for performing studyThere are few studies on the correlations between radiographic measurements of the foot and abnormalities of specific structures found with magnetic resonance imaging (MRI). ObjectivesTo document the relationship between radiographic measurements of the equine foot and the presence of lesions in the foot on MRI. We hypothesised that different radiographic measurements would be associated with specific lesions detected by MRI. Study designRetrospective analysis of radiographs and MRI studies. MethodsSeventy-four feet from 52 lame horses were included. Twenty parameters were measured on radiographs, whereas the signal intensity, homogeneity and size of each structure in the foot were evaluated on magnetic resonance images. The data were analysed using simple linear correlation analysis and classification and regression trees (CARTs). ResultsLinear correlations were found between the navicular bone compacta thickness and injuries of the deep digital flexor tendon, collateral sesamoidean ligament, navicular spongiosa and navicular bone proximal border. Long-toed horses had a high incidence of lesions involving the spongiosa and proximal border of the navicular bone. Elongation of the navicular bone was associated with proximal and distal border injuries. A reduced palmar angle and increased angle between the middle and distal phalanx were observed in horses with alterations of collateral ligaments of the distal interphalangeal joint and navicular bone spongiosa, respectively. For each structure under investigation, CARTs predicting the presence of MRI pathology based on radiographic measurements had excellent performance, with >80% correct classification of cases, when using one of 3 data sources. ConclusionsThis study demonstrated a relationship between radiographic measurements of the foot and the presence of lesions detected on MRI, while CARTs illustrated that different radiographic measurements were associated with different MRI lesions.

关键词： horse magnetic resonance imaging radiography lameness measurements classification and regression tree

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