TÉCNICAS DE INTELIGÊNCIA ARTIFICIAL UTILIZADAS PARA ANÁLISE DA APTIDÃO CARDIORRESPIRATÓRIA: UMA REVISÃO
Palavras-chave:
VO2, Machine Learning, consumo de oxigênioResumo
A aptidão cardiorrespiratória (ACR) pode ser considerada um dos sinalizadores de saúde, estando diretamente ligada a capacidade respiratória e cardiovascular. Este parâmetro permite o estudo do impacto causado por fatores de risco como: obesidade, hipertensão arterial, tabagismo e dislipidemias. Portanto, o processamento rápido e eficiente dos dados provenientes do ACR é de grande importância para a área médica. Ferramentas modernas para análise de dados têm sido desenvolvidas, a fim de caracterizar de forma eficiente conjuntos de dados. Com a ajuda de códigos computacionais que tem como base a teoria do aprendizado automático ou aprendizado computacional, o próprio programa, através do reconhecimento de padrões, desenvolve a capacidade de "aprender". É o caso das técnicas que envolvem "Machine Learning", onde algorítimos complexos avaliam diversas variáveis, utilizando suas próprias respostas para melhora de desempenho. Utilizando os critérios e recomendações dos Itens de Relatórios Preferenciais para Revisões Sistemáticas, o objetivo desse trabalho é avaliar as técnicas de inteligência artificial, denominadas "Machine Learning", que são utilizadas para análises da ACR, revisando os artigos da literatura encontrada no banco de dados PubMed/Medline.
Downloads
Referências
AGGARWAL CC. Data classification: algorithms and applications. CRC Press; 2014. https://www.crcpress.com/Data-Classification-Algorithms-and Applications/Aggarwal/p/book/9781466586741.
ALGHAMDI M, AL-MALLAH M, KETEYIAN S, BRAWNER C, EHRMAN J, SAKR, S (2017) Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. PLoS ONE 12(7):e0179805.
AL-MALLAH et al. Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project). Am J Cardiol 2017;120:2078–2084
BLAIR SN, KOHL HW 3RD, PAFFENBARGER RS JR, CLARK DG, COOPER KH, GIBBONS LW. Physical fitness and all-cause mortality: a prospective study of healthy men and women. JAMA. 1989; 262:2395–401.
BLAIR SN, KAMPERT J, KOHL HW 3rd, et al. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA. 1996; 276:205–10.
BOUCHARD C, SHEPHARD RJ, STEPHENS T. Physical activity, fitness, and health: International proceedings and consensus statement. Champaign: Human Kinetics; 1994.
BREIMAN, L. Machine Learning (2001) 45: 5. Springer.
CHURCH TS, CHENG YJ, EARNEST CP, et al. Exercise capacity and body composition as predictors of mortality among men with diabetes. Diabetes Care. 2004;27:83–8.
CHURCH T, KAMPERT JB, GIBBONS LW, BARLOW CE, BLAIR SN. Usefulness of cardiorespiratory fitness as a predictor of all-cause and cardiovascular disease mortality in men with systemic hypertension. Am J Cardiol. 2001;88(6):651–6.
CRIMINISI, A., SHOTTON, J., KONUKOGLU, E. Foundations and Trends R in Computer Graphics and Vision. Vol. 7, Nos. 2–3 (2011) 81–227 c 2012.
DE’ATH, G, FABRICIUS, K. E. Classification and regression trees. Ecology, Vol. 81, No. 11 (2000).
DUMOND et al. Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning. Eur J Appl Physiol. Online 2017
GREEN J. P. S. Cochrane handbook for systematic reviews of interventions: John Wiley & Sons; 2011.
JOHN et al. Simple to complex modeling of breathing volume using a motion Sensor. Sci Total Environ. Author manuscript; available in PMC 2014 June 01.
KATZMARZYK PT, Church TS, Blair SN. Cardiorespiratory fitness attenuates the effects of the metabolic syndrome on all-cause and cardiovascular disease mortality in men. Arch Intern Med. 2004; 164(10):1092–7.
KODAMA S, SAITO K, TANAKA S, et al. Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis. JAMA. 2009; 301(19):2024–35.
KOKKINOS P, MYERS J, KOKKINOS JP, PITTARAS A, NARAYAN P, MANOLIS A, KARASIK P, GREENBERG M, PAPADEMETRIOU V, SINGH S. Exercise capacity and mortality in black and white men. Circulation 2008;117:614–622.
KOLUS et al. Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches. Applied Ergonomics xxx (2014) 1e9
LAUKKANEN JA, KURL S, SALONEN JT. Cardiorespiratory fitness and physical activity as risk predictors of future atherosclerotic cardiovascular diseases. Curr Atheroscler Rep 2002;4:468–476.
LAUKKANEN JA, RAURAMAA R, SALONEN JT, KURL S. The predictive value of cardiorespiratory fitness combined with coronary risk evaluation and the risk of cardiovascular and all-cause death. J Intern Med 2007;262:263–272.
LAUKKANEN JA, KURL S, SALONEN R, RAURAMAA R, SALONEN JT. The predictive value of cardiorespiratory fitness for cardiovascular events in men with various risk profiles: a prospective population based cohort study. Eur Heart J. 2004;25(16):1428–37.
MOHER D, LIBERATI A, TETZLAFF J, ALTMAN DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Journal of clinical epidemiology. 2009; 62(10):1006–12. https://doi.org/10.1016/j.jclinepi.2009.06.005 PMID: 19631508. 29. Higgins
NAKAMURA et al. Applying Neural Network to VO2 Estimation using 6-axis Motion Sensing Data. 978-1-4577-0220-4/16/$31.00 ©2016 IEEE
NES, B. M., L. J. VATTEN, J. NAUMAN, I. JANSZKY, and U. WISLKFF. A Simple Nonexercise Model of Cardiorespiratory Fitness Predicts Long-Term Mortality. Med. Sci. Sports Exerc., Vol. 46, No. 6, pp. 1159–1165, 2014.
STAMATAKIS E, HAMER M, O’DONOVAN G, BATTY GD, AND KIVIMAKI M. A non-exercise testing method for estimating cardiorespiratory fitness: associations with all-cause and cardiovascular mortality in a pooled analysis of eight population-based cohorts. European Heart Journal (2013) 34, 750–758
SAKR et al. BMC Medical Informatics and Decision Making (2017) 17:174
DOI 10.1186/s12911-017-0566-6
SAKR S, ELSHAWI R, AHMED A, QURESHI WT, BRAWNER C, KETEYIAN S, et al. (2018) Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project. PLoS ONE 13(4): e0195344.
VERIKAS, A., GELZINIS, A., BACAUSKIENE, M. Mining data with random forests: A survey and results of new tests. Pattern Recognition 44 (2011) 330–349
