Data mining: Potential applications in research on nutrition and health

Nutrition and Dietetics

Volumn 74, Issue 1, 2017, Pages 3-10

  Batterham, Marijka ^a   Neale, Elizabeth ^a   Martin, Allison ^a   Tapsell, Linda ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

artificial neural network; classification and regression tree; data mining; support vector machine

Indexed keywords

EID: 85010977146     PISSN: 14466368     EISSN: 17470080     Source Type: Journal    
DOI: 10.1111/1747-0080.12337     Document Type: Article

References (33)

1. Malborough M. Data Mining for Better Protocols. Re-engineering Clinical Trials: Best Practices for Streamlining the Development Process. London: Academic Press, Elsevier, 2015.
2. Brieman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Baton Roca, FL: Chapman and Hall/CRC, 1984.
3. Navarro Silvera SA, Mayne ST, Gammon MD et al. Diet and lifestyle factors and risk of subtypes of esophageal and gastric cancers: classification tree analysis. Ann Epidemiol 2014; 24: 50–57.
4. Lazarou C, Karaolis M, Matalas AL, Panagiotakos DB. Dietary patterns analysis using data mining method. An application to data from the CYKIDS study. Comput Methods Programs Biomed 2012; 108: 706–14.
5. Zhang XC, Zhang ZD, Huang DS. Prediction of length of ICU stay using data-mining techniques: an example of old critically Ill postoperative gastric cancer patients. Asian Pac J Cancer Prev 2012; 13: 97–101.
6. Kastorini CM, Papadakis G, Milionis HJ et al. Comparative analysis of a-priori and a-posteriori dietary patterns using state-of-the-art classification algorithms: a case/case-control study. Artif Intell Med 2013; 59: 175–83.
7. Tapsell LC, Lonergan M, Martin A, Batterham MJ, Neale EP. Interdisciplinary lifestyle intervention for weight management in a community population (HealthTrack study): study design and baseline sample characteristics. Contemp Clin Trials 2015; 45: 394–403.
8. Thomson JL, Landry AS, Zoellner JM et al. Several steps/day indicators predict changes in anthropometric outcomes: HUB City steps. BMC Public Health 2012; 12: 983.
9. Tudor-Locke C, Bassett DR Jr. How many steps/day are enough? Preliminary pedometer indices for public health. Sports Med 2004; 34: 1–8.
10. Williams G. Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. New York, NY: Springer, 2011.
11. Graham J. Missing Data, Analysis and Design. New York, NY: Springer, 2012.
12. Williams G. Rattle: a data mining GUI for R. R J 2009; 1: 45–55.
13. Jenkinson C, Chandola T, Coulter A, Bruster S. An assessment of the construct validity of the SF-12 summary scores across ethnic groups. J Public Health 2001; 23: 187–94.
14. Lovibond SH, Lovibond PH. Manual for the Depression Anxiety Stress Scales – 2nd Edition. Psychology Foundation: Sydney, NSW, 1995.
15. Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45: 23–41.
16. Masters T. Practical Neural Network Recipes in C++. San Diego, CA: Morgan Kaufmann, 1993.
17. Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. J Appl Physiol 2009; 107: 1300–7.
18. Wundersitz DW, Josman C, Gupta R, Netto KJ, Gastin PB, Robertson S. Classification of team sport activities using a single wearable tracking device. J Biomech 2015; 48: 3975–81.
19. Heydari ST, Ayatollahi SM, Zare N. Comparison of artificial neural networks with logistic regression for detection of obesity. J Med Syst 2012; 36: 2449–54.
20. Isma'eel HA, Sakr GE, Almedawar MM et al. Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior. Cardiovasc Diagn Ther 2015; 5: 219–28.
21. Chickering DM, Heckerman D. Fast learning from sparse data. In: Lasky KB, Prade H, eds. Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. Stockholm: Morgan Kaufmann Publishers, 1999; 109–15.
22. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York, NY: Springer, 2009.
23. Fransen HP, May AM, Stricker MD et al. A posteriori dietary patterns: how many patterns to retain? J Nutr 2014; 144: 1274–82.
24. Funtikova AN, Benitez-Arciniega AA, Fito M, Schroder H. Modest validity and fair reproducibility of dietary patterns derived by cluster analysis. Nutr Res 2015; 35: 265–8.
25. Hearty AP, Gibney MJ. Dietary patterns in Irish adolescents: a comparison of cluster and principal component analyses. Public Health Nutr 2013; 16: 848–57.
26. Walthouwer MJ, Oenema A, Soetens K, Lechner L, de Vries H. Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study. Appetite 2014; 82: 154–9.
27. Anil S, Charlton KE, Tapsell LC, Probst Y, Ndanuko R, Batterham M. Identification of dietary patterns associated with blood pressure in a sample of overweight australian adults. J Hum Hypertens 2016; 30: 672–8.
28. Batis C, Mendez MA, Gordon-Larsen P, Sotres-Alvarez D, Adair L, Popkin B. Using both principal component analysis and reduced rank regression to study dietary patterns and diabetes in Chinese adults. Public Health Nutr 2016; 19: 195–203.
29. Thorpe MG, Milte CM, Crawford D, McNaughton SA. A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians. Int J Behav Nutr Phys Act 2016; 13: 30.
30. Khusun H, Fahmida U. Dietary patterns of obese and normal-weight women of reproductive age in urban slum areas in Central Jakarta. Br J Nutr 2016; 116: 1–8.
31. Faraway J. Extending the Linear Model With R: Generalised Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC: Baton Roca, FL, 2005.
32. Green SB. How many subjects does it take to do a regression analysis. Multivariate Behav Res 1991; 26: 499–510.
33. Austin PC, Steyerberg EW. The number of subjects per variable required in linear regression analyses. J Clin Epidemiol 2015; 68: 627–36.