TY - JOUR

T1 - THE RISK MODELING OF DIABETES BASED ON PARAMETRIC AND NONPARAMETRIC BINARY LOGISTIC REGRESSION

AU - Suliyanto,

AU - Rifada, Marisa

N1 - Funding Information:
The authors thanks to the Ministry of Research, Technology and Higher Education of the Republic of Indonesia for financial support of this research through The Fundamental Research University Grant 2019. The authors also thank to the anonymous referees for their valuable suggestions which let to the improvement on the manuscript.
Publisher Copyright:
© 2020. All Rights Reserved.

PY - 2020/10

Y1 - 2020/10

N2 - The parametric binary logistic regression assumes that the logit function is known to be expressed as a linear function in the parameter, while the nonparametric binary logistic regression assumes that the logit function is unknown and can be approximated by the Generalized Additive Model (GAM) or Local Likelihood Logit Estimation (LLLE) method. The GAM method assumes that the logit function is the sum of the nonparametric regression functions of each predictor variable with the known link function. The LLLE method assumes that the logit function is a linear function in the parameter, where the parameters depend on arbitrary fixed points and the likelihood logit function depends on the multivariate kernel weighting. In this study we compared the risk prediction of diabetes based on three approaches, i.e parametric binary logistic regression, nonparametric binary logistic regression using the GAM method, and nonparametric binary logistic regression with the LLLE method. The results of classification accuracy in risk prediction of diabetes using the parametric binary logistic regression approach of 80.2%, the GAM method of 88.89%, and the LLLE method of 100%. So, the best approach model is obtained by nonparametric binary logistic regression with the LLLE method.

AB - The parametric binary logistic regression assumes that the logit function is known to be expressed as a linear function in the parameter, while the nonparametric binary logistic regression assumes that the logit function is unknown and can be approximated by the Generalized Additive Model (GAM) or Local Likelihood Logit Estimation (LLLE) method. The GAM method assumes that the logit function is the sum of the nonparametric regression functions of each predictor variable with the known link function. The LLLE method assumes that the logit function is a linear function in the parameter, where the parameters depend on arbitrary fixed points and the likelihood logit function depends on the multivariate kernel weighting. In this study we compared the risk prediction of diabetes based on three approaches, i.e parametric binary logistic regression, nonparametric binary logistic regression using the GAM method, and nonparametric binary logistic regression with the LLLE method. The results of classification accuracy in risk prediction of diabetes using the parametric binary logistic regression approach of 80.2%, the GAM method of 88.89%, and the LLLE method of 100%. So, the best approach model is obtained by nonparametric binary logistic regression with the LLLE method.

KW - diabetes

KW - GAM

KW - local likelihood logit estimation

KW - nonparametric binary logistic regression

KW - parametric binary logistic regression

UR - http://www.scopus.com/inward/record.url?scp=85097180971&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85097180971

SN - 1819-6608

VL - 15

SP - 2356

EP - 2363

JO - ARPN Journal of Engineering and Applied Sciences

JF - ARPN Journal of Engineering and Applied Sciences

IS - 20

ER -