TY - GEN
T1 - Modeling of diabetes mellitus risk based on consumption of salt, sugar, and fat factors using local linear estimator
AU - Anam, W. A.
AU - Massaid, A.
AU - Amesya, N. A.
AU - Chamidah, N.
N1 - Publisher Copyright:
© 2020 American Institute of Physics Inc.. All rights reserved.
PY - 2020/9/22
Y1 - 2020/9/22
N2 - Diabetes Mellitus is a major health problem in the world. Diabetes Mellitus, commonly known as “the silent killer”, affects many of the body’s systems and even leads to other serious diseases. The data from global studies showed that the number of people with Diabetes Mellitus in 2011 reached 366 million from all over the world. As a noncommunicable disease, the prevalence of diabetes rises every year. Unhealthy eating habits, such as the consumption of salt, sugar and an excessive amount of fats, is one of the inflicting factors of this disease. For predicting diabetes mellitus risk based on salt, sugar and fat consumptions, we need to build a model. In statistical analysis, there are two approaches for estimating the model, i.e., parametric and nonparametric regression model. A local linear estimator is one of the estimators in nonparametric regression model that the advantages of this estimator can estimate the function at each point such that the model closes to the real pattern, and also no need large data to estimate the model. In this paper, we estimate the diabetes mellitus risk model based on salt, sugar and fat consumptions by using local linear estimator and compare it with logistic parametric regression approach. The result of this study, we get classification accuracies of diabetes mellitus risk based on salt, sugar and fat consumptions of 94.28% by using local linear estimator and of 80% by using parametric logistic regression. It means that nonparametric regression model approach by using local linear estimator is better than parametric logistic regression model approach.
AB - Diabetes Mellitus is a major health problem in the world. Diabetes Mellitus, commonly known as “the silent killer”, affects many of the body’s systems and even leads to other serious diseases. The data from global studies showed that the number of people with Diabetes Mellitus in 2011 reached 366 million from all over the world. As a noncommunicable disease, the prevalence of diabetes rises every year. Unhealthy eating habits, such as the consumption of salt, sugar and an excessive amount of fats, is one of the inflicting factors of this disease. For predicting diabetes mellitus risk based on salt, sugar and fat consumptions, we need to build a model. In statistical analysis, there are two approaches for estimating the model, i.e., parametric and nonparametric regression model. A local linear estimator is one of the estimators in nonparametric regression model that the advantages of this estimator can estimate the function at each point such that the model closes to the real pattern, and also no need large data to estimate the model. In this paper, we estimate the diabetes mellitus risk model based on salt, sugar and fat consumptions by using local linear estimator and compare it with logistic parametric regression approach. The result of this study, we get classification accuracies of diabetes mellitus risk based on salt, sugar and fat consumptions of 94.28% by using local linear estimator and of 80% by using parametric logistic regression. It means that nonparametric regression model approach by using local linear estimator is better than parametric logistic regression model approach.
UR - http://www.scopus.com/inward/record.url?scp=85092592643&partnerID=8YFLogxK
U2 - 10.1063/5.0023498
DO - 10.1063/5.0023498
M3 - Conference contribution
AN - SCOPUS:85092592643
T3 - AIP Conference Proceedings
BT - Symposium on Biomathematics 2019, SYMOMATH 2019
A2 - Apri, Mochamad
A2 - Akimenko, Vitalii
PB - American Institute of Physics Inc.
T2 - Symposium on Biomathematics 2019, SYMOMATH 2019
Y2 - 25 August 2019 through 28 August 2019
ER -