A Scalable Feature Extraction Technique to enhance Multivariable Linear Regression Model for Empirically Derived Patterns-Cereals

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-6
Year of Publication : 2019
Authors : S.HimaVarsha, D.Rajeswara Rao
DOI :  10.14445/22312803/IJCTT-V67I6P114


MLA Style:S.HimaVarsha, D.Rajeswara Rao "A Scalable FeatureExtraction Technique to enhance Multivariable Linear Regression Model for Empirically Derived Patterns-Cereals" International Journal of Computer Trends and Technology 67.6 (2019): 85-88.

APA Style S.HimaVarsha, D.Rajeswara Rao. A Scalable Feature Extraction Technique to enhance Multivariable Linear Regression Model for Empirically Derived Patterns-CerealsInternational Journal of Computer Trends and Technology, 67(6),85-88.

In India Dietary patterns (DPs) are heterogeneous and data on association of indigenous with risk factors of nutrition-related non communicable diseases (cardiovascular disease and diabetes), leading causes of premature death and disability, are limited. To evaluate the institutions of empirically-derived DPs with blood lipids, fasting glucose and blood strain ranges in an adult Indian populace. This is to study empirical nutritional patterns in adults and their association with socio demographic characteristics, life-style elements, self-pronounced nutrient intake, nutrient biomarkers, and the Nutrient-based Diet Quality Score (NDQS) the use of National Diet and Nutrition Survey records. Feature extraction technique used to reduce noisy facts and increase the accuracy of the system. In the existing device the patterns have genuine correlations between HEI and blood nutrients which could were because of every day variability within the HEI and the biomarker concentrations and actually, better than those stated. Our proposed system is robust standard error multivariable linear regression models were used to verify the association of DP’s. Principal component analysis (PCA) was used to investigate major DPs based on Eigen value> 1 and component interpretability. In this Ant Colony Algorithm is used for construction of empirically derived dietary patterns and the result generated gives the best solution for cereals.

[1] Katharine Roberts , Janet Cade; Empirically Derived Dietary Patterns in UK Adults Are Associated with Sociodemographic Characteristics, Lifestyle, and Diet Quality ,2018
[2] Willett, W.C.; McCullough, M.L. Dietary pattern analysis for the evaluation of dietary guidelines. Asia Pac. J. Clin.Nutr. 2008, 17 (Suppl. 1), 75–78
[3] Stephanie J. Weinstein , PhD; Tara M. Vogt, MPH, PhD; Shirley A. Gerrior, PhD, RD , Healthy Eating Index Scores Are Associated with Blood Nutrient Concentrations in the Third National Health and Nutrition Examination Survey,2007.
[4] Hu, F.B. Dietary pattern analysis: A new direction in nutritional epidemiology. Curr.Opin.Lipidol. 2002, 13, 3–9.
[5] S. Kashef and H. Nezamabadi-pour,“An advanced ACO algorithm for feature subset selection”, Neurocomputing, pp.271-279, 2015.
[6] M. Dorigo and T. Strutzle, “Ant colony optimization,” MIT Press, Cambridge, MA, 2004
[7] U. Boryczka and J. Kozak. Ant Colony Decision Trees – A New Method for Constructing Decision Trees Based on Ant Colony Optimization. In Proceedings of the ICCCI, pages 373–382, 2010. [8] M. Dorigo, G.D. Caro, and L.M. Gambardella, “Ant algorithms for discrete optimization,” Artificial Life, vol. 5, no. 2, page 137, 1999

Dietary patterns, Fasting glucose, Feature extraction, Principal Component Analysis PCA.