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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika JIPM (Jurnal Ilmiah Pendidikan Matematika) MaPan : Jurnal Matematika dan Pembelajaran Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Cendekia : Jurnal Pendidikan Matematika FIBONACCI: Jurnal Pendidikan Matematika dan Matematika J-ADIMAS (Jurnal Pengabdian kepada Masyarakat) JP2M (Jurnal Pendidikan dan Pembelajaran Matematika)
Hasyim, Maylita
Universitas Bhinneka PGRI
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Physics

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Nonlinear Ordinal Logistic Regression and Multivariate Adaptive Regression Splines (NORL-MARS) for Prediction of Diabetes Mellitus Risk Any Tsalasatul Fitriyah; Maylita Hasyim; Nur Chamidah; Toha Saifudin; Vita Fibriyani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28733

Abstract

Diabetes Mellitus (DM) is a high-risk metabolic disease with increasing prevalence in Indonesia, requiring an effective classification model based on significant risk factors. This study uses Nonparametric Ordinal Logistic Regression based on the Multivariate Adaptive Regression Spline estimator (NOLR-MARS). Unlike conventional parametric ordinal regression, this model does not assume a fixed functional pattern but rather determines the form of the relationship based on data patterns through basis functions, making it more flexible in handling complex predictor variable interactions. Using 664 records from the Non-Alcoholic Fatty Liver Disease (NAFLD) cohort, we explore the relationship between metabolic factors, included age, sex, Body Mass Index (BMI), LDL cholesterol, and hypertension—and DM risk. This NOLR-MARS integration addresses the nonlinear relationship while maintaining the ordinal nature of DM stages, a combination often overlooked in traditional models. Based on Generalized Cross Validation (GCV) selection, the best model achieved 74.92% accuracy for in-sample data and 80.30% for out-sample data. Furthermore, a sensitivity of 70% and a specificity of 92.86% were obtained for stage 2 DM. Factors such as age, BMI, LDL cholesterol, and hypertension significantly influenced DM status. The results showed that the NORL-MARS model had good predictive performance. The novelty of this study lies in the integration of the MARS estimator into an ordinal logistic regression framework for more granular DM risk assessment. Although this model shows potential as a screening tool in high-risk metabolic cohorts, further clinical application requires external validation to ensure broader generalizability.
Co-Authors 'Ulum, Wisda Miftakhul A, Elby Putra Ahmad Khoirul Umam Any Tsalasatul Fitriyah Azhmi Irtsan Rasyidin Desty Wukandari Diana, Erna Putri Febrika Kusuma Andreina Henny Fathul Kurniawati Laylatul Fitri Lenny Nuurotun Mashlihah M. Joang Equator Sudjono Mukaromah, Sri Jamilah Nadella Neno Christian Dharu Nanda Fitri Nur Rohmah Novia Frisda Eldiana Nur Chamidah Nurrohmah, Armita Purnomo, Agus Purnomo Ridwan, Dina Rosyida Siti Zainab Sri Jamilah Mukaromah Sudjono, M. Joang Equator Sugeng Romadhoni Tanisa Diva Aryani Toha Saifudin Tomi Listiawan Varidho Bagus AriSetyo Subekti VITA FIBRIYANI VITA FIBRIYANI, VITA Widia Nurhasanah Widya Krismon Sri Palupi Wisda Miftakhul 'Ulum