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Journal : International Journal of Basic and Applied Science

Longitudinal Alzheimer’s Disease Progression Modelling Using Adaptive Spline Regression Harahap, Muhammad Khoiruddin; Hendraputra, Surya
International Journal of Basic and Applied Science Vol. 14 No. 3 (2025): Optimization and Artificial Intelligence
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i3.748

Abstract

Alzheimer’s disease is one of the most prevalent neurodegenerative disorders, and modeling its longitudinal progression is essential for improving early intervention and clinical decision-making. While spline-based approaches have been widely used to capture nonlinear patterns, their application to longitudinal Alzheimer’s progression remains limited, particularly with respect to adaptive knot selection and clinical interpretability. This study addresses this gap by applying adaptive spline regression with automatic knot selection via Generalized Cross Validation (GCV) to longitudinal Alzheimer’s disease modeling. Using a simulated longitudinal dataset of 200 patients explicitly designed to reflect realistic clinical characteristics such as cognitive decline (MMSE), hippocampal volume change, and APOE ε4 genetic status we systematically evaluate the proposed method under controlled conditions. The adaptive spline model is compared against linear regression and static (fixed-knot) spline regression using 5-fold cross-validation. The results show that adaptive spline regression achieves lower RMSE (0.191) and MAE (0.152), and a higher R² (0.130) than the baseline models. Although the explained variance remains modest, the adaptive spline more effectively captures nonlinear progression patterns and yields smoother, clinically interpretable trajectories. These findings demonstrate that adaptive knot selection enhances both flexibility and interpretability in longitudinal disease modeling. From a practical perspective, the resulting progression curves have potential value for exploratory clinical analysis and hypothesis generation. Future work will focus on validating the framework using real-world datasets such as OASIS and ADNI, and extending the model to incorporate multimodal biomarkers for improved clinical relevance.
Co-Authors . Zulfan AA Sudharmawan, AA Abdul Samad Adidtya Perdana, Adidtya Aditya, Vikra Afriani, Dina Amir Mahmud Husein Amir Mahmud Husein, Amir Mahmud Amir Mahmud Husein, Mawaddah Harahap, Amir Amsar Yunan Amsar, Amsar Anugreni, Fera Ariany, Vince Arie Budiansyah Aritonang, Romulo P. Atabiq, Fauzun Ayesha Muazzam AYU LESTARI Candra, Rudi Arif Clawdia, Jhessica Dian Pratiwi, Aulya Dicky Apdilah Diding Kusnady Dimas Sasongko Dina Afriani Eko Pramono Epi, Yus Erwinsyah Sipahutar Evan Afri Evan Afri Fachrul Rozi Lubis Ferdy Riza Firnanda, Ary Ginting, Rico Imanta Handayani, Saskia Hantono Hantono Haris Lubis, Abdul Hariyanti, Irma Hendraputra, Surya Herry Setiawan Herry Setiawan Ilham, Dirja Nur Indra Surya, Indra Indra, Jamaludin Intan Maulina, Intan Jannah, Dina Miftahul Jhessica Clawdia Juanda Hakim Lubis Khairuman Khairuman Man Lubis, Fachrul Rozi Maharina, Maharina Maqfirah Mhd Zulfansyuri Siambaton Miza, Khairul Mohammed Saad Talib Muhammad Hamza Muhammad Rian Almadani Mursidah natasha, Syarifah fadillah Natasya, Syarifah Fadillah Novita, Hilda Yulia Nursila Nursila Nursila, Nursila Nurul Khairina Nurul Khairina Nurul Khairina Pania, Sadri Paryono, Tukino Patel , Hrishitva Permata, Riski Surya Ramadhan, M. Irfan Rina Rina Rizki, Lutfi Trisandi Rizky, Muharratul Mina Rosihana, Riscki Elita S.SE,MM, Yanti Salsa Dilah Cicilia Putri Sandi Pratama Saputra, Devi Satria Sepri Kurniadi Sihabudin Sihabudin, Sihabudin Sridewi, Nurmala Surya Hendraputra Syifa Setiawan, Muhammad Afdhalu Talib, Mohammed Saad Talib, Muhammed Saat Urmila, Tasya Wahyudi Lubis Xu, Chlap Min Zhu, Kong Huang Zonyfar, Candra