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Des Suryani
Universitas Islam Riau
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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A Hybrid Approach of Factor Analysis and Decision Tree for Epilepsy Onset Prediction Using Public Datasets Eka Sabna; Des Suryani; Oktavia Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7253

Abstract

Early prediction of epilepsy onset is crucial for supporting timely clinical intervention and reducing seizure-related complications. However, the high dimensionality and complexity of Electroencephalogram (EEG) and clinical data remain major challenges for conventional predictive models. This study proposes an interpretable hybrid framework integrating Exploratory Factor Analysis (EFA) and Decision Tree classification for early epilepsy onset prediction using public datasets. EFA was employed to reduce 15 observed variables into five clinically meaningful latent factors, which were subsequently used as inputs for the Decision Tree model. . According to experimental data, the suggested framework demonstrated steady classification performance with an accuracy of 88.14% and specificity of 88.73%, while its efficacy in identifying epilepsy beginning cases for early screening is highlighted by its sensitivity of 80.77%.. The latent factor representing clinical neurological abnormalities was identified as the most influential predictor in the classification process. Compared with conventional black-box machine learning approaches, the proposed model provides transparent decision rules and clinically meaningful interpretation while maintaining reliable predictive capability. All things considered, the suggested architecture is a viable path for creating clinically interpretable prediction models that facilitate transparent and trustworthy early epilepsy screening.
Co-Authors Abdul Syukur Ambiyar, Ambiyar Ana Yulianti Ana Yulianti Asrul Huda Ause Labellapansa, Ause Dafwen Toresa Dedy Irfan Deny Jollyta Dwi Elvira Eka Sabna Elsa Lutfi Maghfiroh Fitri Ayu Fitri Ayu Ganefri . Hanafiah, Anggi Hendra Gunawan Jepri Alber Muhammad Muhammad Muhammad Zulhelmi Mundayani Mundayani Mutia Fadhilla Nesi Syafitri Oktavia Dewi Sabna, Eka Sinta Maria Taslim Taslim Yudhi Arta Zulfajri Zulfajri