Soft Voting Based Optimized Ensemble for Migraine Type Classification

Titik  Misriati; Riska Aryanti; Henny  Leidiyana

doi:10.33365/jatika.v6i3.861

Titik Misriati Universitas Bina Sarana Informatika
Riska Aryanti Universitas Bina Sarana Informatika
Henny Leidiyana Universitas Bina Sarana Informatika

DOI: https://doi.org/10.33365/jatika.v6i3.861

Keywords: Migraine Classification, Ensemble Learning, Soft Voting, Machine Learning, Clinical Decision Support

Abstract

The accurate classification of migraine subtypes is a complex challenge in neurology, hindered by symptomatic similarities between types. This complexity necessitates advanced computational tools to support diagnostic precision. This study aims to develop and evaluate an optimized soft voting ensemble classifier to automate this multi-class classification task effectively. The methodology involved training eight base models—including Neural Network, Random Forest, and Gradient Boosting—on a publicly available migraine dataset, with an 80-20 train-test split. The top three performers were integrated into a soft voting ensemble, which aggregates their predicted probabilities to enhance decision robustness. Model performance was rigorously assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results demonstrated that the proposed ensemble achieved superior performance, with an accuracy of 91.67% and an F1-score of 91.50%, outperforming all constituent models. Furthermore, the ensemble attained near-perfect AUC-ROC values across multiple classes, confirming its strong discriminatory capability. The study concludes that the soft voting ensemble is a highly effective and reliable approach for migraine subtype classification, offering significant potential as a decision-support tool in clinical environments. Future work will focus on hyperparameter optimization, explainability, and validation with larger multi-centric datasets to facilitate clinical adoption.

Downloads

Download data is not yet available.

References

M. F. P. Peres et al., “Migraine is the most disabling neurological disease among children and adolescents, and second after stroke among adults: A call to action,” Cephalalgia, vol. 44, no. 8, Aug. 2024, doi: 10.1177/03331024241267309.

T. J. Steiner, L. J. Stovner, R. Jensen, D. Uluduz, and Z. Katsarava, “Migraine remains second among the world’s causes of disability, and first among young women: findings from GBD2019,” J. Headache Pain, vol. 21, no. 1, p. 137, Dec. 2020, doi: 10.1186/s10194-020-01208-0.

N. Magnavita, “Headache in the Workplace: Analysis of Factors Influencing Headaches in Terms of Productivity and Health,” Int. J. Environ. Res. Public Health, vol. 19, no. 6, p. 3712, Mar. 2022, doi: 10.3390/ijerph19063712.

M. Leonardi and A. Raggi, “A narrative review on the burden of migraine: when the burden is the impact on people’s life,” J. Headache Pain, vol. 20, no. 1, p. 41, Dec. 2019, doi: 10.1186/s10194-019-0993-0.

T. J. Steiner et al., “Migraine-attributed burden, impact and disability, and migraine-impacted quality of life: Expert consensus on definitions from a Delphi process,” Cephalalgia, vol. 42, no. 13, pp. 1387–1396, Nov. 2022, doi: 10.1177/03331024221110102.

A. K. Eigenbrodt et al., “Diagnosis and management of migraine in ten steps,” Nat. Rev. Neurol., vol. 17, no. 8, pp. 501–514, Jun. 2021, doi: 10.1038/s41582-021-00509-5.

A. A. Garstka et al., “Accurate Diagnosis and Treatment of Painful Temporomandibular Disorders: A Literature Review Supplemented by Own Clinical Experience,” Pain Res. Manag., vol. 2023, pp. 1–12, Jan. 2023, doi: 10.1155/2023/1002235.

J. A. Edlow, “Misdiagnosis of Acute Headache,” Emerg. Med. Clin. North Am., vol. 43, no. 1, pp. 67–80, Feb. 2025, doi: 10.1016/j.emc.2024.05.026.

C. Baccouch and C. Bahar, “Advanced Machine Learning Approaches for Accurate Migraine Prediction and Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 16, no. 1, 2025, doi: 10.14569/IJACSA.2025.0160101.

L. Khan, M. Shahreen, A. Qazi, S. Jamil Ahmed Shah, S. Hussain, and H.-T. Chang, “Migraine headache (MH) classification using machine learning methods with data augmentation,” Sci. Rep., vol. 14, no. 1, p. 5180, Mar. 2024, doi: 10.1038/s41598-024-55874-0.

M. Yuichi and Y. A. Susetyo, “Klasifikasi Penyakit Migrain dengan Metode Naïve Bayes pada Dataset Kaggle,” J. Indones. Manaj. Inform. dan Komun., vol. 6, no. 1, pp. 139–151, Jan. 2025, doi: 10.35870/jimik.v6i1.1150.

A. Reddy and A. Reddy, “Migraine triggers, phases, and classification using machine learning models,” Front. Neurol., vol. 16, May 2025, doi: 10.3389/fneur.2025.1555215.

C. Baccouch and C. Bahar, “Advanced Machine Learning Approaches for Accurate Migraine Prediction and Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 16, no. 1, 2025, doi: 10.14569/ijacsa.2025.0160101.

W. Lee, I. K. Min, K. I. Yang, D. Kim, C.-H. Yun, and M. K. Chu, “Classifying migraine subtypes and their characteristics by latent class analysis using data of a nation-wide population-based study,” Sci. Rep., vol. 11, no. 1, p. 21595, Nov. 2021, doi: 10.1038/s41598-021-01107-7.

Y. Garcia-Chimeno, B. Garcia-Zapirain, M. Gomez-Beldarrain, B. Fernandez-Ruanova, and J. C. Garcia-Monco, “Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data,” BMC Med. Inform. Decis. Mak., vol. 17, no. 1, Apr. 2017, doi: 10.1186/s12911-017-0434-4.

A. Rojarath and W. Songpan, “Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems,” Appl. Intell., vol. 51, no. 7, pp. 4908–4932, Jul. 2021, doi: 10.1007/s10489-020-02106-3.

S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier,” Int. J. Cogn. Comput. Eng., vol. 2, 2021, doi: 10.1016/j.ijcce.2021.01.001.

K. A. Pratama, M. Nasution, and M. H. Munandar, “Sistem Informasi Pendaftaran Imunisasi Posyandu Anggrek Di Desa Tanjung Medan Berbasis Web,” pp. 27–33, 2020.

W. Zhang, D. Yang, and S. Zhang, “A new hybrid ensemble model with voting-based outlier detection and balanced sampling for credit scoring,” Expert Syst. Appl., vol. 174, p. 114744, Jul. 2021, doi: 10.1016/j.eswa.2021.114744.

J. R. Campos, E. Costa, and M. Vieira, “Improving Failure Prediction by Ensembling the Decisions of Machine Learning Models: A Case Study,” IEEE Access, vol. 7, pp. 177661–177674, 2019, doi: 10.1109/ACCESS.2019.2958480.

I. D. Mienye and Y. Sun, “A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects,” IEEE Access, vol. 10, pp. 99129–99149, 2022, doi: 10.1109/ACCESS.2022.3207287.

P. Boozary, S. Sheykhan, H. GhorbanTanhaei, and C. Magazzino, “Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction,” Int. J. Inf. Manag. Data Insights, vol. 5, no. 1, p. 100331, Jun. 2025, doi: 10.1016/j.jjimei.2025.100331.

V. N. Thatha et al., “Optimized machine learning mechanism for big data healthcare system to predict disease risk factor,” Sci. Rep., vol. 15, no. 1, p. 14327, Apr. 2025, doi: 10.1038/s41598-025-98721-6.