1,461
Views
6
CrossRef citations to date
0
Altmetric
Reviews

Applicability of machine learning techniques in food intake assessment: A systematic review

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon

References

  • Ahn, J. S., D. W. Kim, J. Kim, H. Park, and J. E. Lee. 2019. Development of a smartphone application for dietary self-monitoring. Frontiers in nutrition 6:1–12. doi:10.3389/fnut.2019.00149.
  • Al-Maqaleh, B. M., and A. M. G. Abdullah. 2017. Intelligent predictive system using classification techniques for heart disease diagnosis. International Journal of Computer Science Engineering (IJCSE) 6 (6):145–51.
  • Assari, R. P., Azimi, and M. R. Taghva. 2017. Heart disease diagnosis using data mining techniques. International Journal of Economics & Management Sciences 06 (03):1–5. doi:10.4172/2162-6359.1000415.
  • Babu, S., E. M. Vivek, K. P. Famina, K. Fida, P. Aswathi, M. Shanid, and M. Hena. 2017. Heart disease diagnosis using data mining technique. Electronics, Communication and Aerospace Technology (ICECA), International Conference 1:750–3.
  • Bodnar, L. M., A. R. Cartus, S. I. Kirkpatrick, K. P. Himes, E. H. Kennedy, H. N. Simhan, W. A. Grobman, J. Y. Duffy, R. M. Silver, S. Parry, et al. 2020. Machine learning as a strategy to account for dietary synergy: An illustration based on dietary intake and adverse pregnancy outcomes. The American Journal of Clinical Nutrition 111 (6):1235–43. doi:10.1093/ajcn/nqaa027.
  • Burgermaster, M., J. H. Son, P. G. Davidson, A. M. Smaldone, G. Kuperman, D. J. Feller, K. G. Burt, M. E. Levine, D. J. Albers, C. Weng, et al. 2020. A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study. International Journal of Medical Informatics 139:104158. doi:10.1016/j.ijmedinf.2020.104158.
  • Chmurzynska, A., M. A. Mlodzik-Czyzewska, A. M. Malinowska, J. Czarnocinska, and D. J. Wiebe. 2018. Use of a smartphone application can improve assessment of high-fat food consumption in overweight individuals. Nutrients 10 (11):1692–12. doi:10.3390/nu10111692.
  • ColorBrewer. 2020. Programa Color Brewer 2.0 Color Advice for cartography. Disponpivelem. Accessed 2020. https://colorbrewer2.org/.
  • Cutillo, C. M., K. R. Sharma, L. Foschini, S. Kundu, M. Mackintosh, and K. D. Mand. 2020. Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency. NPJ Digital Medicine 3:47. doi:10.1038/s41746-020-0254-2.
  • Dao, M. C., A. F. Subar, M. Warthon-Medina, J. E. Cade, T. Burrows, R. K. Golley, N. G. Forouhi, M. Pearce, and B. A. Holmes. 2019. Dietary assessment toolkits: An overview. Public Health Nutrition 22 (3):404–418. doi:10.1017/S1368980018002951.
  • De Cos Juez, F. J., F. S. Lasheras, P. J. G. Nieto, and M. A. S. Suárez. 2009. A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in postmenopausal women. International Journal of Computer Mathematics 86 (10-11):1878–87. doi:10.1080/00207160902783557.
  • De Cos Juez, F. J. M A., Suárez-Suárez, F. S. Lasheras, and A. Murcia-Mazón. 2011. Application of neural networks to the study of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Mathematical and Computer Modelling 54 (7-8):1665–1670. doi:10.1016/j.mcm.2010.11.069.
  • Dey, A. 2016. Machine learning algorithms: A review. International Journal of Computer Science and Information Technologies 7 (3):1174–1179.
  • Dipnall, J. F. J A., Pasco, M. Berk, L. J. Williams, S. Dodd, F. N. Jacka, and D. Meyer. 2017. Getting RID of the blues: Formulating a Risk Index for Depression (RID) using structural equation modeling. Australian & New Zealand Journal of Psychiatry 51 (11):1121–13. doi:10.1177/0004867417726860.
  • Easton, J. F. H R., Sicilia, and C. R. Stephens. 2019. Classification of diagnostic subcategories for obesity and diabetes based on eating patterns. Nutrition & Dietetics: The Journal of the Dietitians Association of Australia 76 (1):104–109. doi:10.1111/1747-0080.12495.
  • Faruqui, S. H. A. Y., Du, R. Meka, A. Alaeddini, C. Li, S. Shirinkam, and J. Wang. 2019. Development of a deep learning model for dynamic forecasting of blood glucose level for type 2 diabetes mellitus: Secondary analysis of a randomized controlled trial. JMIR mHealth and uHealth 7 (11):e14452. doi:10.2196/14452.
  • Fernandes, F. T., and A. D. P. C. Filho. 2019. Data mining and machine learning perspectives for occupational safety and health. Revista Brasileira de Saúde Ocupacional 44:e13. doi:10.1590/2317-6369000019418.
  • Forman, E. M. S P., Goldstein, R. J. Crochiere, M. L. Butryn, A. S. Juarascio, F. Z. Zhang, and G. D. Foster. 2019. Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss. Translational Behavioral Medicine 6:1–13.
  • Forman, E. M. S P., Goldstein, F. Zhang, B. C. Evans, S. M. Manasse, M. L. Butryn, A. S. Juarascio, P. Abichandani, G. J. Martin, and G. D. Foster. 2019. OnTrack: Development and feasibility of a smartphone app designed to predict and prevent dietary lapses. Translational Behavioral Medicine 9 (2):236–245. doi:10.1093/tbm/iby016.
  • Ghorbani, R., and R. Ghousi. 2019. Predictive data mining approaches in medical diagnosis: A review of some diseases prediction. International Journal of Data and Network Science 3:47–70. doi:10.5267/j.ijdns.2019.1.003.
  • Giabbanelli, P. J., and J. Adams. 2016. Identifying small groups of foods that can predict achievement of key dietary recommendations: Data mining of the uk national diet and nutrition survey, 2008-12. Public Health Nutrition 19 (9):1543–1551. doi:10.1017/S1368980016000185.
  • Goldstein, B. A., A. M. Navar, and R. E. Carter. 2017. Moving beyond regression techniques in cardiovascular risk prediction: Applying machine learning to address analytic challenges. European Heart 38 (23):1805–1814.
  • Guan, V. X. Y C., Probst, E. P. Neale, M. J. Batterham, and L. C. Tapsell. 2018. Identifying usual food choices at meals in overweight and obese study volunteers: Implications for dietary advice. The British Journal of nutrition 120 (4):472–480. doi:10.1017/S0007114518001587.
  • Hamad, R., Z. S. Templeton, L. Schoemaker, M. Zhao, and J. Bhattacharya. 2019. Comparing demographic and health characteristics of new and existing SNAP recipients: Application of a machine learning algorithm. The American Journal of clinical nutrition 109 (4):1164–1172. doi:10.1093/ajcn/nqy355.
  • He, X. B R., Matam, S. Bellary, G. Ghosh, and A. K. Chattopadhyay. 2020. CHD risk minimization through lifestyle control: Machine learning gateway. Scientific Reports 10 (1):4090. doi:10.1038/s41598-020-60786-w.
  • Hearty, A. P., and M. J. Gibney. 2008. Analysis of meal patterns with the use of supervised data mining techniques-artificial neural networks and decision trees. The American Journal of Clinical Nutrition 88 (6):1632–42. doi:10.3945/ajcn.2008.26619.
  • Higgins, J. P. T., and S. Green. 2011. Cochrane handbook for systematic reviews of interventions. West Sussex, England: John Wiley & Sons.
  • Iwendi, C. S., Khan, J. H. Anajemba, A. K. Bashir, and F. Noor. 2020. Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model. IEEE Access 8:28462–28474. doi:10.1109/ACCESS.2020.2968537.
  • Jain, A. K. 2010. Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31 (8):651–666. doi:10.1016/j.patrec.2009.09.011.
  • Jia, W. Y., Li, R. Qu, T. Baranowski, L. E. Burke, H. Zhang, Y. Bai, J. M. Mancino, G. Xu, Z.-H. Mao, et al. 2019. Automatic food detection in egocentric images using artificial intelligence technology. Public Health Nutrition 22 (7):1168–1179. doi:10.1017/S1368980018000538.
  • Jiang, L., K. Audouze, J. A. R. Herrera, L. H. Angquist, S. K. Kjaerulff, J. M. G. Izarzugaza, A. Tjønneland, J. Halkjaer, K. Overvad, T. I. A. Sørensen, et al. 2020. Conflicting associations between dietary patterns and changes of anthropometric traits across subgroups of middle-aged women and men. Clinical Nutrition (Edinburgh, Scotland) 39 (1):265–275. doi:10.1016/j.clnu.2019.02.003.
  • Kan, H. J., H. Kharrazi, H.-Y. Chang, D. Bodycombe, K. Lemke, and J. P. Weiner. 2019. Exploring the use of machine learning for risk adjustment: A comparison of standard and penalized linear regression models in predicting health care costs in older adults. PLoS One 14 (3):e0213258. doi:10.1371/journal.pone.0213258.
  • Kanerva, N. J., Kontto, M. Erkkola, J. Nevalainen, and S. Mannisto. 2018. Suitability of random forest analysis for epidemiological research: Exploring sociodemographic and lifestyle-related risk factors of overweight in a cross-sectional design. Scandinavian Journal of Public Health 46 (5):557–564. doi:10.1177/1403494817736944.
  • Khan, A. B., Baharudin, L. H. Lee, and K. Khan. 2010. A review of machine learning algorithms for text-documents classification. Journal of advances in information technology 1 (1):4–20.
  • Kodati, S. R., Vivekanandam, and G. Ravi. 2019. Comparative analysis of clustering algorithms with heart disease data sets using data mining weka tool. Soft Computing and Signal Processing 111–117.
  • Kwon, Y-JH S., Kim, D.-H. Jung, and J.-K. Kim. 2020. Cluster analysis of nutritional factors associated with low muscle mass index in middle-aged and older adults. Clinical Nutrition (Edinburgh, Scotland) 39 (11):3369–3376. doi:10.1016/j.clnu.2020.02.024.
  • Lakshmi, K. S., and G. Vadivu. 2017. Extracting association rules from medical health records using multi-criteria decision analysis. Procedia Computer Science 115:290–95.
  • Latha, R., and T. Thegaleesan. 2019. Complexity of food choice and statistical techniques. International Journal of Innovative Studies in Sociology and Humanities 4 (2):90–95.
  • Lazarou, C., M. Karaolis, A.-L. Matalas, and D. B. Panagiotakos. 2012. Dietary patterns analysis using data mining method. An application to data from the CYKIDS study. Computer Methods and programs in biomedicine 108 (2):706–714. doi:10.1016/j.cmpb.2011.12.011.
  • Liberati, A. D G., Altman, J. Tetzlaff, C. Mulrow, P. C. Gøtzsche, J. P. A. Ioannidis, M. Clarke, P. J. Devereaux, J. Kleijnen, and D. Moher. 2009. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. Research Methods & Reporting 339:b2700. doi:10.1136/bmj.b2700.
  • Lundberg, S. M., and S.-I. Lee. 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems 4765–4774.
  • Ma, S., and X. Chen. 2019. A data mining approach to predict risk of cardiovascular. AIP Conference Proceedings.
  • Matloff, N. 2009. The art of R programming: A tour of statistical software design. 373 p.
  • Mezgec, S., and B. K. Seljak. 2017. NutriNet: A deep learning food and drink image recognition system for dietary assessment. Nutrients 9 (7):657. doi:10.3390/nu9070657.
  • Michalski, R. S., J. G. Carbonell, and T. M. Mitchell. 2013. Machine learning: An artificial intelligence approach. Springer Science & Business Media.
  • Murrell, P. 2005. R graphics. 1st ed. Editora: Chapman and Hall/CRC; 328 p.
  • Mutter, S. A. E., Casey, S. Zhen, Z. Shi, and V.-P. Mäkinen. 2017. Multivariable analysis of nutritional and socio-economic profiles shows differences in incident anemia for Northern and Southern Jiangsu in China. Nutrients 9 (10):1153. doi:10.3390/nu9101153.
  • Narziev, N. H., Goh, K. Toshnazarov, S. A. Lee, K.-M. Chung, and Y. Noh. 2020. STDD: Short-term depression detection with passive sensing. Sensors 20 (5):1396. doi:10.3390/s20051396.
  • Ordóñez, C. J M., Matías, J. F. De Cos Juez, and P. J. García. 2009. Machine learning techniques applied to the determination of osteoporosis incidence in post-menopausal women. Mathematical and Computer Modelling 50 (5-6):673–679. doi:10.1016/j.mcm.2008.12.024.
  • Pagamunici, L., M. De Souza, A. H. P. Gohara, A. K. Silvestre, A. A. F. Visentainer, J. V. De Souza, N. E. Gomes, and S. T. M. Matsushita. 2014. Multivariate study and regression analysis of gluten-free granola. Food Science and Technology 34 (1):127–134. doi:10.1590/S0101-20612014005000005.
  • Panaretos, D. E., Koloverou, A. C. Dimopoulos, G.-M. Kouli, M. Vamvakari, G. Tzavelas, C. Pitsavos, and D. B. Panagiotakos. 2018. A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002-2012): The ATTICA study. The British Journal of Nutrition 120 (3):326–334. doi:10.1017/S0007114518001150.
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg. 2011. Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research 12:2825–2830.
  • Popkin, B. M. L S., Adair, and S. W. Ng. 2012. Global nutrition transition and the pandemic of obesity in developing countries. Nutrition Reviews 70 (1):3–21. doi:10.1111/j.1753-4887.2011.00456.x.
  • Rajput, A. R P., Aharwal, M. Dubey, S. Saxena, and M. Raghuvanshi. 2011. J48 and JRIP rules for e-governance data. International Journal of Computer Science and Security 5 (2):201–207.
  • Reis, R. H., Peixoto, J. Machado, and A. Abelha. 2017. Machine learning in nutritional follow-up research. Open Computer Science 7 (1):41–45. doi:10.1515/comp-2017-0008.
  • Rodriguez-Galiano, V., M. Sanchez-Castillo, M. Chica-Olmo, and M. Chica-Rivas. 2015. Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews 71:804–818. doi:10.1016/j.oregeorev.2015.01.001.
  • Rosso, N., and P. Giabbanelli. 2018. Accurately inferring compliance to five major food guidelines through simplified surveys: Applying data mining to the UK National Diet and Nutrition Survey. JMIR Public Health and surveillance 4 (2):e56. doi:10.2196/publichealth.9536.
  • Rupasinghe, W. S. W. A., H. T. S. Perera, and N. M. J. Wickramaratne. 2020. A comprehensive review on dietary assessment methods in epidemiological research. Public Health Nutrition 3 (1):204–211.
  • Savage, A. H., Bambrick, and D. Gallegos. 2020. From garden to store: Local perspectives of changing food and nutrition security in a Pacific Island country. Food Security 12 (6):1331–1348.
  • Shao, Z., C. Chen, W. Li, H. Ren, and W. Chen. 2019. Assessment of the risk factors in the daily life of stroke patients based on an optimized decision tree. Technology and health care: Official journal of the European Society for Engineering and Medicine 27 (S1):317–S329. doi:10.3233/THC-199030.
  • Sharp, D. B., and M. Allman-Farinelli. 2014. The feasibility and validity of mobile phones to assess dietary intake. Nutrition (Burbank, Los Angeles County, Calif.) 30 (11-12):1257–1266. doi:10.1016/j.nut.2014.02.020.
  • Shiao, S. P. K., J. Grayson, A. Lie, and C. H. Yu. 2018a. Personalized nutrition—Genes, diet, and related interactive parameters as predictors of cancer in multiethnic colorectal cancer families. Nutrients 10 (6):795. doi:10.3390/nu10060795.
  • Shiao, S. P. K., J. Grayson, A. Lie, and C. H. Yu. 2018b. Predictors of the healthy eating index and glycemic index in multi-ethnic colorectal cancer families. Nutrients 10 (6):674. doi:10.3390/nu10060674.
  • Shim, J.-S., K. Oh, and H. C. Kim. 2014. Dietary assessment methods in epidemiologic studies. Epidemiology and health 36:e 2014009. doi:10.4178/epih/e2014009.
  • Shiokawa, Y., Y. Date, and J. Kikuchi. 2018. Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet. Scientific Reports 8 (1):3426. doi:10.1038/s41598-018-20121-w.
  • Silva, B. V. R., M. G. Rad, J. Cui, M. Mccabe, and K. Pan. 2018. A mobile-based diet monitoring system for obesity management. Journal of Health & Medical Informatics 9 (2):1–20.
  • Silvera, S. A. N., S. T. Mayne, M. D. Gammon, T. L. Vaughan, W.-H. Chow, J. A. Dubin, R. Dubrow, J. L. Stanford, A. B. West, H. Rotterdam, et al. 2014. Diet and lifestyle factors and risk of subtypes of esophageal and gastric cancers: Classification tree analysis. Annals of epidemiology 24 (1):50–57. doi:10.1016/j.annepidem.2013.10.009.
  • Singh, P. S., Singh, and G. S. Pandi-Jai. 2018. Effective heart disease prediction system using data mining techniques. International Journal of nanomedicine 13:121–124. doi:10.2147/IJN.S124998.
  • Siqueira-Batista, R., and E. Silva. 2019. Notas sobre os fundamentos matemáticos da Inteligência Artificial. Revista De Ciência, Tecnologia e Inovação 4:44–54.
  • Smallwood, R. D., and E. J. Sondik. 1973. The optimal control of partially observable Markov processes over a finite horizon. Operations Research 21 (5):1071–1088. doi:10.1287/opre.21.5.1071.
  • Tan, P. N., M. Steinbach, and V. Kumar. 2006. Introduction to data mining. São Carlos: Pearson Education.
  • Vasileska, A., and G. Rechkoska. 2012. Global and regional food consumption patterns and trends. Procedia - Social and Behavioral Sciences 44:363–369. doi:10.1016/j.sbspro.2012.05.040.
  • Vucic, V., M. Glibetic, R. Novakovic, J. Ngo, D. Ristic-Medic, J. Tepsic, M. Ranic, L. Serra-Majem, and M. Gurinovic. 2009. Dietary assessment methods used for low-income populations in food consumption surveys: A literature review. British Journal of Nutrition 101 (S2):S95–S101. doi:10.1017/S0007114509990626.
  • Xu, R., B. E. Blanchard, J. M. McCaffrey, S. Woolley, L. M. L. Corso, and V. B. Duffy. 2020. Food liking-based diet quality indexes (DQI) generated by conceptual and machine learning explained variability in cardiometabolic risk factors in young adults. Nutrients 12 (4):882. doi:10.3390/nu12040882.
  • Yu, E. Y. W., A. Wesselius, C. Sinhart, A. Wolk, M. C. Stern, X. Jiang, L. Tang, J. Marshall, E. Kellen, P. van den Brandt, et al. 2020. A data mining approach to investigate food groups related to incidence of bladder cancer in the bladder cancer epidemiology and nutritional determinants international study. The British Journal of nutrition 124 (6):611–619. doi:10.1017/S0007114520001439.
  • Zeevi, D., T. Korem, N. Zmora, D. Israeli, D. Rothschild, A. Weinberger, O. Ben-Yacov, D. Lador, T. Avnit-Sagi, M. Lotan-Pompan, et al. 2015. Personalized nutrition by prediction of glycemic responses. Cell 163 (5):1079–1094. doi:10.1016/j.cell.2015.11.001.
  • Zenitani, S. H., Nishiuchi, and T. Kiuchi. 2010. Smart-card-based automatic meal record system intervention tool for analysis using data mining approach. Nutrition Research (New York, N.Y.) 30 (4):261–270. doi:10.1016/j.nutres.2010.04.003.
  • Zheng, Q., H. Delingette, K. Fung, S. E. Petersen, and N. Ayache. 2019. Unsupervised shape and motion analysis of 3822 cardiac 4D MRI of UK Biobank. Preprint submitted toarXiv.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.