Multivariate Adaptive Generalized Poisson Regression Spline (MAGPRS) on the number of acute respiratory infection infants

B. W. Otok*, S. Hidayati, Purhadi

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


Acute Respiratory Infection (ARI) is an infectious disease of the respiratory tract that affects the structure of the respiratory tract. The ARI is a health problem that should not be ignored because it causes high infant mortality. Therefore, it is important to know the factors that influence the ARI. Generalized Poisson Regression (GPR) is one of method that can handle cases of overdispersion (variance is greater than the mean) or underdispersion (variance is less than the mean). Multivariate Adaptive Regression Spline as a statistical method for fitting the relationship between a set of input variables and dependent variables. This research is the development of the MARS method and GPR namely MAGPRS. The application of the MAGPRS model was carried out in the case of the number of ARI in infants from surabaya health departement 2017. The results showed that the importance of predictor variables in MAGPRS, the variables affecting the number of ARI patients in infants are the percentage of low birth weight (X2), the percentage of unhealthy houses (X5), and the percentage given non-exclusive breastfeeding to infants (X1).

Original languageEnglish
Article number012062
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 19 Dec 2019
Event6th International Conference on Research, Implementation, and Education of Mathematics and Science, ICRIEMS 2019 - Yogyakarta, Indonesia
Duration: 12 Jul 201913 Jul 2019


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