DOI: https://doi.org/10.14710/j.gauss.15.2.379-390

NONPARAMETRIC SPLINE REGRESSION APPROACH IN CASE STUDY OF MATERNAL MORTALITY RATES IN SOUTH SULAWESI

*Nur Salsabila Wero  -  Postgraduate Student in Department Statistics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar, South Sulawesi, Indonesia, Indonesia
Rahmat Hidayat  -  Department Statistics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar, South Sulawesi, Indonesia, Indonesia
Ruliana Ruliana  -  Department Statistics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Makassar, South Sulawesi, Indonesia, Indonesia
Open Access Copyright 2026 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Maternal Mortality Rate (MMR) is an important indicator of maternal health and the quality of healthcare services. Its relationship with maternal health-related conditions may be nonlinear, requiring a flexible modeling approach. This study aims to model MMR using nonparametric spline regression and identify the optimal knot configuration for the predictor variables. The response variable was MMR, while the predictor variables consisted of Bleeding (X₁), Hypertension Disorders (X₂), Post-Miscarriage Complications (X₃), and Heart and Blood Vessel Disorders (X₄). Secondary data were obtained from the South Sulawesi Provincial Health Office for 24 districts/cities in 2023. The optimal knot configuration was determined using the Generalized Cross-Validation (GCV) criterion. The selected model used one knot for X₁, three knots for X₂, two knots for X₃, and one knot for X₄. The parameter significance test showed that Bleeding (X₁) was the only predictor with a statistically significant linear component at the 5% significance level, with a p-value of 0.0006, while the other linear and spline components were not statistically significant. The model achieved an MSE of 0.9388, RMSE of 0.9689, MAPE of 15.6494%, and R² of 0.9653. These results indicate that nonparametric spline regression flexibly models relationships between MMR and selected maternal health-related conditions effectively.
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Keywords: Maternal Mortality Rate; Nonparametric Regression; Spline Regression; Knot Point; Generalized Cross Validation (GCV)

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