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