Multi-method approach to wellness predictive modeling

Agarwal, Ankur; Baechle, Christopher; Behara, Ravi S.; Rao, Vinaya

doi:10.1186/s40537-016-0049-0

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…ML techniques can be used to identify variables associated with an outcome as the number of variables increase. ML techniques have been applied to survey data to identify variables that are associated with different psychological and disease conditions [ 41 , 42 , 43 , 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Atuegwu

Oncken

Laubenbacher

et al. 2020

IJERPH

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E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18–34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

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Section: Introductionmentioning

confidence: 99%

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Atuegwu

Oncken

Laubenbacher

et al. 2020

IJERPH

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“…Note that the overall research design takes on an experimental approach. On the other hand, [51,52] adopt a systems development approach to their studies, where predictive modeling is a sub-task. They provide prototypes as their main outcomes, thereby providing design recommendations along with their predictive contributions.…”

Section: Data Science As a Research Methods (Rm)mentioning

confidence: 99%

Data science: developing theoretical contributions in information systems via text analytics

Rizk

Elragal

2020

J Big Data

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Just like in business and society, data in research is increasing in volume, velocity and variety, and requires new ways of extracting value from it. Data science (DS)-the systematic extraction of knowledge from data-has been attracting a lot of attention recently [1]. It is argued that data science is leading a new scientific paradigm [2, 3]. Its epistemological assumptions, challenges and opportunities have been discussed in various disciplines [4, 5]. However, there are also questions about whether it is really a new (fourth) paradigm of science or empiricism re-emerging [6], or simply an extension of existing paradigms with new tools and methods for scientific enquiry [7].

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“…Consequently, the outputs of such a network must be transformed into some understandable form, conditioned by the learning process, which, as a rule, is not a difficult task. [11] Currently, foreign academic journals publish results of applied research in the field of economics and sociology, based on the use of neural network technologies and machine learning, including methodological and practical aspects of forecasting economic growth [12].…”

Section: Materials and Methods (Model)mentioning

confidence: 99%

Regional Life Quality Management: Methodological Approaches to its Forecasting using Neural Networks

Петрова¹

2018

Proceedings of the International Scientific Conference "Competitive, Sustainable and Secure Development of the Regional Economy

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Abstract-This paper proposes theoretical and methodical approaches to forecasting of quality of life in regions using neural networks. Assessment of quality of life main indices is a subject of study of both federal statistical agencies and several expert organizations. However, an adequate measurement of these indicators is confronted with information and methodological problems, which include: the incompatibility of methods of measurements in Soviet Union and modern Russian statistics; incomplete pools of data associated with various negative processes, administrative reforms and institutional changes in the Russian economy; partial loss of historical statistical data. This determines the inability to use time series models of economic indicators to identify the main development patterns, and the resulting trends are limited and not reliable enough. Furthermore, most methods have applicability limits and do not work with incomplete or "noisy" data. Therefore, it is proposed to use neural network technologies and intelligent information systems developed on their basis as alternative approach for solving semistructured and unformalized problems of analyzing the quality of life, as well as constructing forecasting models. A comparative analysis of the researches of Russian and foreign authors on the problems of assessment and constructing a integral indicator of the quality of life of the population is carried out. The author proposes an assessment model, as well as the results of a neural network construction, and an economic interpretation of the obtained results.

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Multi-method approach to wellness predictive modeling

Cited by 11 publications

References 38 publications

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

Data science: developing theoretical contributions in information systems via text analytics

Regional Life Quality Management: Methodological Approaches to its Forecasting using Neural Networks

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