Abstract
In this digital age, data is generated monstrous from diverse sources like IoT enabled smart gadgets, and so on worldwide very swiftly in distinctive formats. This data with the traits say volume, velocity, variety and so on referred to as big data. Since a decade, big data technologies have been utilized in most of the companies even in healthcare alongside IoT to gain treasured insights in making knowledgeable selections spontaneously to improve medical treatment particularly for patients with complicated medical history having multiple health ailments. For healthy living, after water and oxygen, diet plays a critical role in offering the strength needed to assist the life’s existence-maintaining strategies and also the vitamins needed to construct and keep all body cells. The intent of this work is to offer a framework that classifies the population into four classes based on the quality of diet they devour within 30-days of dietary recall as balanced, unbalanced, nearly balanced, and nearly unbalanced using the machine learning techniques specifically logistic regression, linear discriminant analysis (LDA), and random forest. NHANES datasets had been used to assess the proposed framework alongside the metrics accuracy, precision, etc. This framework also allows us in gathering person’s health and dietary details dynamically anytime with the voice (IoT) to find out to which food regimen the person belongs to. This could be pretty beneficial for a person, medical doctors, and dieticians as nicely.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2(1):3
Akil L, Ahmad HA Relationships between obesity and cardiovascular diseases in four southern states and Colorado, https://doi.org/10.1353/hpu.2011.0166
Gartner IT Glossary (n.d.) Retrieved from http://www.gartner.com/it-glossary/big-data/
Akred J Founder and CTO, silicon valley data science. What is big data? https://datascience.berkeley.edu/
Zikopoulos PC, Eaton C, deRoos D, Deutsch T, Lapis G (2012) Understanding big data. McGraw-Hill, New York
Tomines A, Readhead H, Readhead A, Teutsch S (2013) Applications of electronic health information in public health: uses, opportunities and barriers. eGEMs (Generating evidence & methods to improve patient outcomes)1(2), Article 5. Doi http://dx.doi.org/10.13063/2327-9214.1019
Rajendra N et al (2015) Modern diet and its impact on human health. J Nutr Food Sci 5:6. https://doi.org/10.4172/2155-9600.1000430
Vilchis-Gil J et al (2015) Food habits, physical activities and sedentary lifestyles of eutrophic and obese school children: a case–control study. BMC Public Health 15:124. https://doi.org/10.1186/s12889-015-1491-1
Carruthers K Internet of things and beyond: cyber-physical systems. IEEE Internet of Things, 10 May 2016, https://iot.ieee.org/newsletter/may-2016/internet-of-things-and-beyond-cyber-physical-systems.html. Retrieved 26 Dec 2017
Schatz B (2015) National surveys of population health: big data analytics for mobile health monitors. Big Data 3:219–229. https://doi.org/10.1089/big.2015.0021
http://healthyeating.sfgate.com/differencee-between-balanceddiet-unbalanced-diet-10916.html
Nilufer Hajra, Worldwide phenomenon: poor diet linked to death, global study reveals, 20th Sep 2017
Parthasarathy KS (2017) Childhood and adolescent obesity increases tenfold in four decades–analysis. Eurasia Rev News Anal http://www.eurasiareview.com/12102017-childhood-and-adolescent-obesity-increases-tenfold-in-four-decades-analysis/. Retrieved on 11 Nov 2017
Webber L, Kilpi F, Marsh T, Rtveladze K, Brown M, McPherson K (2017) High rates of obesity and non-communicable diseases predicted across Latin America. Barengo NC, ed. PLoS ONE. 2012;7(8):e39589. https://doi.org/10.1371/journal.pone.0039589
Aisha M Mapped: the global epidemic of ‘lifestyle’ disease in charts. The Telegraph News, 29th Mar 2018, https://www.telegraph.co.uk/news/0/mapped-global-epidemic-lifestyle-disease-charts/. Retrieved on 13 May 2018
Mehtha N, Pandit A (2018) Concurrence of big data analytics and healthcare: a systematic review. Int J Med Inf 114:57–65
Healthcare Technology Review: 2017, referral md, https://getreferralmd.com/2017/01/17-future-healthcare-technology-advances-of-2017-referralmd/. Retrieved 14 May 2018
Clark A, Ng JQ, Morlet N, Semmens JB (2016) Big data and ophthalmic research. Surv Ophthalmol 61:443–465. https://doi.org/10.1016/j.survophthal.2016.01.003
Chen M, Hao Y, Hwang K, Wang L, Wang L (2017) Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5:8869–8879. https://doi.org/10.1109/access.2017.2694446
Dinachandra Singh K, Alagarajan M, Ladusingh L (2015) What explains child malnutrition of indigenous people of Northeast India? PLoS ONE 10(6):e0130567. https://doi.org/10.1371/journal.pone.0130567
Ahluwalia N, Dwyer J, Terry A, Moshfegh A, Johnson C (2016) Update on NHANES dietary data: focus on collection, release, analytical considerations, and uses to inform public policy. Adv Nutr 7(1):121–134. https://doi.org/10.3945/an.115.009258
Guenther PM, Kirkpatrick SI, Reedy J, Krebs-Smith SM, Buckman DW, Dodd KW, Casavale KO, Carroll RJ (2014) J Nutr 144(3):399–407. Published online 2014 Jan 22. doi: https://doi.org/10.3945/jn.113.183079.PMCID:PMC3927552
Hearty AP, Gibney MJ Analysis of meal patterns with the use of supervised data mining techniques—artificial neural networks and decision trees, https://doi.org/10.3945/ajcn.2008.26619
Dezhi X, Ganegoda GU et al (2011) Rule based classification to detect malnutrition in children. Int J Comput Sci Eng (IJCSE)3(1). ISSN: 0975-3397
Park M, Kim H, Kim SK (2014) Knowledge discovery in a community data set: malnutrition among the elderly. Healthc Inf Res 20(1):30–38
NHANES-National Health and Nutrition Examination Survey. http://www.cdc.gov/nchs/nhanes/index.htm
Oracle Text Application Developer’s Guide 12c Release 1, E41398-07, May 2015
Pang Ning Tan MS (2006) Introduction to data mining. Pearson Education Asia Ltd., China P. R
Polamuri S How multinomial logistic regression model works in machine learning https://dataaspirant.com/2017/03/14/multinomial-logistic-regression-model-works-machine-learning. Retrieved on 29th June 2017
Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer
Olson DL, Delen D (2008) Advanced data mining techniques, 1st edn. Springer (1 Feb 2008), p 138. ISBN 3-540-76916-1
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/a:1010933404324
Chakraborty DP (2010) Prediction accuracy of a sample-size estimation method for ROC studies. Acad Radiol 17(5):628–638. https://doi.org/10.1016/j.acra.2010.01.007
Kajaree D, Behera R (2017) A survey on healthcare monitoring system using body sensor network. Int J Innov Res Comput Commun Eng 5(2):1302–1309
http://www.faqs.org/nutrition/Met-Obe/National-Health-and-Nutrition-Examination-Survey-NHANES.html
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Mani, J.J.S., Rani Kasireddy, S. (2019). Population Classification upon Dietary Data Using Machine Learning Techniques with IoT and Big Data. In: Social Network Forensics, Cyber Security, and Machine Learning. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-1456-8_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-1456-8_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1455-1
Online ISBN: 978-981-13-1456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)