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Gusti, Siska Kurnia
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : bit-Tech

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Thyroid Disease Classification Using Support Vector Machine and Recursive Feature Elimination Method Citra Wulandari; lis Afrianty; Elvia Budianita; Siska Kurnia Gusti
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3454

Abstract

Thyroid disease is a common endocrine disorder that can cause serious metabolic and cardiovascular complications, so accurate early detection is clinically essential. This study proposes a Support Vector Machine (SVM) classifier enhanced with Recursive Feature Elimination (RFE) to select the most informative attributes and Adaptive Synthetic Sampling (ADASYN) to handle class imbalance in a Kaggle thyroid dataset of 3,771 clinical records. The data contain 25 diagnostic attributes with a strongly skewed distribution between healthy and thyroid cases. The model’s robustness was examined using three train–test split ratios. The best configuration, SVM with a Linear kernel and 20 RFE-selected features under an 80:20 split, achieved 98.39% accuracy, with precision, recall, and F1-score all reaching 0.98, indicating consistently strong performance across classes. RFE contributes by removing redundant or weakly relevant variables, helping the classifier construct a more stable and interpretable decision boundary. ADASYN further improves the representation of the minority class, yielding higher recall and F1-score for thyroid cases and reducing the risk of missed diagnoses. Overall, the combined use of feature selection and adaptive oversampling produces a balanced and computationally efficient model for thyroid disease classification. These findings suggest that the proposed approach can support clinical decision-making, reduce diagnostic errors in imbalanced data settings, and strengthen early detection efforts in endocrine health assessment. By offering high sensitivity for thyroid cases while maintaining robust specificity for healthy patients, the model is well suited for integration into clinical decision-support and routine screening workflows.
Co-Authors Abdul Wahid Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Adi Mustofa Al Rasyid, Nabila Alfaiza, Raihan Zia Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Amelia, Felina Anggi Vasella Azhima, Mohd Baehaqi Beni Basuki Citra Wulandari Cut Lira Kabaatun Nisa Destri Putri Yani Devi Julisca Sari Dina Septiawati Dinyah Fithara efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Erni Rouza, Erni Fadhilah Syafria Faska, Ridho Mahardika Febi Yanto Fitri Insani Fitri Insani Fitri Wulandari Fitri, Anisa Gusti, Gogor Putra Hafi Puja Hanif, Wan Muhammad Iis Afrianty Iis Afrianty Iqbal Salim Thalib Irsyad (Scopus ID: 57204261647), Muhammad Iwan Iskandar Jasril Jasril Jasril Jasril Khair, Nada Tsawaabul Kurniansyah, Juliandi Lestari Handayani lis Afrianty M Wandi Dwi Wirawan Maemonah, Maemonah Morina Lisa Pura Muhammad Affandes Muhammad Affandes Muhammad Fauzan Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi Nazir, Alwis Norhiza, Fitra Lestari Novriyanto Novriyanto Nurul Ikhsan Okfalisa Okfalisa Pizaini Pizaini Prima Yohana Rahmah Miya Juwita Raja Indra Ramoza Ramadhani, Astrid Risfi Ayu Sandika Robbi Nanda Robby Azhar Sardi, Hajra Satria Bumartaduri Sayyid Muhammad Habib Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Syafria, Fadhillah Syahbudin Hamwar Syaputra, Muhammad Dwiky Umam, Isnaini Hadiyul Vitriani, Yelfi Vusuvangat, Imam Wulandari, Fitri Yayuk Wulandari Yelfi Yelfi Yola, Melfa Yusra Yusra, - Yusra, Yusra