Proceedings of International Conference on Applied Innovation in IT
2025/12/22, Volume 13, Issue 5, pp.659-669

Hybrid AdaBoost-PSO Model for Thyroid Disease Diagnosis


Ammar A. Kazm, Zamen Abood Ramadhan, Jaafar Sadiq Alrubaye, Iman Kadhim Ajlan, Jamal kh-madhloom, Eshaq Aziz Awadh AL Mandhari, Fahad Taha AL-Dhief, Nurhizam Safie and Ali Hashim Abbas


Abstract: The endocrine thyroid gland is an essential organ for regulating metabolism, production of primarily triiodothyronine (T3) and thyroxine (T4) which are necessary for digestion, heart rate and their imbalance could result in metabolism disorder. Diagnosis of thyroid diseases could be frustrated, If the lab techniques are used. This study was designed to use classical machine learning (ML) to improve the accuracy and precision of thyroid symptoms diagnosis using UCI thyroid dataset. The applied stage includes handling missing values using the K-Nearest Neighbors (KNN) imputation method. At the second stage to address class imbalance, we applied three data resampling method: Random Under Sampling, SMOTE-SVM, and K-Means-SMOTE, and many of the classical machine learning algorithms were utilized including Logistic Regression, AdaBoost, SGD, SVM, and KNN. Additionally, a hybrid AdaBoost-PSO (Particle Swarm Optimization) model was merged to enhance classification performance in terms of accuracy, precision, recall, F1-score, mcc, roc confusion matrix, true positive, false positive, true negative, and false negative. The results showed that K-Means-SMOTE + AdaBoost + PSO pipeline model achieves accuracy of 99.73%, precision of 0.991, recall of 98.40%, F1-score of 98.80%, MCC of 97.60%, and an AUC score of 98.40%. The corresponding confusion matrix indicated excellent classification capability with 64 true positives, 1 false positive, 2 false negatives, and 1065 true negatives as compared with other models. As well as the heat map analysis showed that thyroid-stimulating hormone (TSH) has a high level and both free thyroxine index (FTI) and T4 have a low level among the numbers of patients.

Keywords: AdaBoost-PSO, Random Under Sampling, SMOTE-SVM, K-Means-SMOTE, Machine Learning, Thyroid Diseases.

DOI: Under indexing

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References:

  1. S. Shroff, S. Pise, P. Chalekar, and S. S. Panicker, “Thyroid disease diagnosis: A survey,” in 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), IEEE, 2015, pp. 1-6, [Online]. Available: https://doi.org/10.1109/ISCO.2015.7282384.
  2. Y. Ma et al., “A study of machine learning models for rapid intraoperative diagnosis of thyroid nodules for clinical practice in China,” Cancer Medicine, 2024, [Online]. Available: https://doi.org/10.1002/cam4.6854.
  3. X. Chai, “Diagnosis method of thyroid disease combining knowledge graph and deep learning,” IEEE Access, vol. 8, pp. 149787-149795, 2020, [Online]. Available: https://doi.org/10.1109/ACCESS.2020.3016676.
  4. S. Talebi et al., “Trace element status and hypothyroidism: A systematic review and meta-analysis,” Biological Trace Element Research, vol. 197, pp. 1-14, 2020, [Online]. Available: https://link.springer.com/article/10.1007/s12011-019-01963-5.
  5. Y. Qing et al., “Circulatory trace element variations in Alzheimer’s disease: A systematic review and meta-analysis,” Environmental Sciences Europe, vol. 36, no. 1, p. 148, 2024, [Online]. Available: http://dx.doi.org/10.1186/s12302-024-00980-z.
  6. U. R. Acharya et al., “Thyroid lesion classification in 242 patient population using Gabor transform features from high resolution ultrasound images,” Knowledge-Based Systems, vol. 107, pp. 235-245, 2016, [Online]. Available: https://doi.org/10.1016/j.knosys.2016.06.010.
  7. J. Y. Noh et al., “An evaluation of the efficacy of machine learning in predicting thyrotoxicosis and hypothyroidism: A comparative assessment of biochemical test parameters used in different health checkups,” Internal Medicine, 2024, [Online]. Available: https://doi.org/10.2169/internalmedicine.2825-23.
  8. H. Mousa and A. Zoori, “Prevalence of thyroid disorders in Nasiriya City, Iraq,” University of Thi-Qar Journal of Science, vol. 10, no. 1, pp. 122-127, 2023, [Online]. Available: http://dx.doi.org/10.32792/utq/utjsci/v10i1.1040.
  9. K. Sindhya, “Effective prediction of hypothyroid using various data mining techniques,” EPRA International Journal of Research and Development, vol. 5, no. 2, 2020, [Online]. Available: https://doi.org/10.36713/epra2016.
  10. S. Gothane, “Data mining classification on hypothyroids detection: Association women outnumber men,” International Journal of Recent Technology and Engineering, vol. 8, no. 16, pp. 601-604, 2020, [Online]. Available: http://dx.doi.org/10.35940/ijrte.F7261.038620.
  11. K. Chandel, V. Kunwar, S. Sabitha, T. Choudhury, and S. Mukherjee, “A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques,” CSI Transactions on ICT, vol. 4, pp. 313-319, 2016, [Online]. Available: https://doi.org/10.1007/s40012-016-0100-5.
  12. F. T. Al-Dhief, N. M. A. Latiff, M. M. Baki, N. N. N. A. Malik, N. Sabri, and M. A. A. Albadr, “Voice pathology detection using support vector machine based on different number of voice signals,” in 2021 26th IEEE Asia-Pacific Conference on Communications (APCC), Kuala Lumpur, Malaysia, 2021, pp. 1-6, , doi: 10.1109/APCC49754.2021.9609830.
  13. M. A. A. Albadr, F. T. Al-Dhief, Man, L. et al., “Online sequential extreme learning machine approach for breast cancer diagnosis,” Neural Computing and Applications, vol. 36, pp. 10413-10429, 2024, [Online]. Available: https://doi.org/10.1007/s00521-024-09617-x.
  14. N. A. N. Za’im, F. T. Al-Dhief, M. Azman, M. R. M. Alsemawi, N. M. A. Abdul Latiff, and M. Mat Baki, “The accuracy of an online sequential extreme learning machine in detecting voice pathology using the Malaysian voice pathology database,” Journal of Otolaryngology-Head & Neck Surgery, vol. 52, no. 1, 2023, [Online]. Available: https://doi.org/10.1186/s40463-023-00661-6.
  15. H. Lu and S. Uddin, “Unsupervised machine learning for disease prediction: A comparative performance analysis using multiple datasets,” Health and Technology, pp. 1-14, 2023, [Online]. Available: https://doi.org/10.1016/j.rineng.2022.100778.
  16. I. F. Dehkordi, K. Manochehri, and V. Aghazarian, “Internet of Things (IoT) intrusion detection by machine learning (ML): A review,” Asia-Pacific Journal of Information Technology & Multimedia, vol. 12, no. 1, 2023, [Online]. Available: https://doi.org/10.17576/apjitm-2023-1201-02.
  17. N. Ananthi et al., “Detecting six different types of thyroid diseases using deep learning approaches,” in 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), IEEE, 2022, pp. 1-8, [Online]. Available: https://doi.org/10.1109/ACCAI53970.2022.9752581.
  18. P. Gupta et al., “Detecting thyroid disease using optimized machine learning model based on differential evolution,” International Journal of Computational Intelligence Systems, vol. 17, no. 1, p. 3, 2024, [Online]. Available: https://doi.org/10.1007/s44196-023-00388-2.
  19. N. M. A. A. Latiff et al., “Voice pathology detection using machine learning algorithms based on different voice databases,” Results in Engineering, vol. 25, p. 103937, 2025, [Online]. Available: https://doi.org/10.1016/j.rineng.2025.103937.
  20. M. A. A. Albadr, M. Ayob, S. Tiun, R. Z. Homod, F. T. Al-Dhief, and M. H. Mutar, “Parkinson’s disease diagnosis by voice data using particle swarm optimization-extreme learning machine approach,” Multimedia Tools and Applications, vol. 84, no. 23, pp. 26843-26876, 2025, [Online]. Available: https://doi.org/10.1007/s11042-024-20108-y.
  21. F. T. Al-Dhief et al., “Voice pathology detection and classification by adopting online sequential extreme learning machine,” IEEE Access, vol. 9, pp. 77293-77306, 2021, , doi: 10.1109/ACCESS.2021.
  22. D. Umar Sidiq, S. M. Aaqib, and R. A. Khan, “Diagnosis of various thyroid ailments using data mining classification techniques,” International Journal of Scientific Research in Computer Science and Information Technology, vol. 5, pp. 131-136, 2019, [Online]. Available: https://doi.org/10.32628/CSEIT195119.
  23. E. Sonuç, “Thyroid disease classification using machine learning algorithms,” in Journal of Physics: Conference Series, vol. 1963, no. 1, IOP Publishing, 2021, p. 012140, [Online]. Available: http://dx.doi.org/10.1088/1742-6596/1963/1/012140.
  24. S. R. Rad, Z. H. Mohammadi, M. J. Zadeh, M. A. Mosleh-Shirazi, and T. Dehesh, “Identification of important symptoms and diagnostic hypothyroidism patients using machine learning algorithms,” Annals of Medicine and Surgery, vol. 86, no. 6, pp. 3233-3241, 2024, [Online]. Available: http://dx.doi.org/10.1097/MS9.0000000000002068.
  25. S. Sankar, A. Potti, G. N. Chandrika, and S. Ramasubbareddy, “Thyroid disease prediction using XGBoost algorithms,” Journal of Mobile Multimedia, vol. 18, no. 3, pp. 1-18, 2022, [Online]. Available: http://dx.doi.org/10.1088/1742-6596/1963/1/012140.
  26. T. A. Alawiyah, T. Wibisono, and Y. S. Mulyani, “The prediction of thyroid cancer recurrence with the XGBoost method: The clinicopathological feature-based approach,” Journal of Computer Networks, Architecture and High Performance Computing, vol. 6, no. 3, pp. 1035-1045, 2024, [Online]. Available: https://doi.org/10.47709/cnahpc.v6i3.4101.
  27. S. Dalal et al., “Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques,” Multimedia Tools and Applications, 2024, [Online]. Available: https://doi.org/10.1007/s11042-024-19713-8.
  28. S. S. Islam, M. S. Haque, M. S. U. Miah, T. B. Sarwar, and R. Nugraha, “Application of machine learning algorithms to predict the thyroid disease risk: An experimental comparative study,” PeerJ Computer Science, vol. 8, p. e898, 2022, [Online]. Available: http://dx.doi.org/10.7717/peerj-cs.898.
  29. S. Borzouei, H. Mahjub, N. A. Sajadi, and M. Farhadian, “Diagnosing thyroid disorders: Comparison of logistic regression and neural network models,” Journal of Family Medicine and Primary Care, vol. 9, no. 3, pp. 1470-1476, 2020, [Online]. Available: http://dx.doi.org/10.4103/jfmpc.jfmpc_910_19.
  30. L. Aversano et al., “Thyroid disease treatment prediction with machine learning approaches,” Procedia Computer Science, vol. 192, pp. 1031-1040, 2021, [Online]. Available: https://doi.org/10.1016/j.procs.2021.08.106.
  31. T. Alyas, M. Hamid, K. Alissa, T. Faiz, N. Tabassum, and A. Ahmad, “Empirical method for thyroid disease classification using a machine learning approach,” BioMed Research International, vol. 2022, 2022, [Online]. Available: https://doi.org/10.1155/2022/9809932.
  32. G. Akgül, A. A. Çelik, Z. E. Aydin, and Z. K. Öztürk, “Hipotiroidi hastalığı teşhisinde sınıflandırma algoritmalarının kullanımı,” Bilişim Teknolojileri Dergisi, vol. 13, no. 3, pp. 255-268, 2020, [Online]. Available: https://doi.org/10.17671/gazibtd.710728.
  33. Y. H. Shakir, “Thyroid disease dataset,” [Online]. Available: https://www.kaggle.com/datasets/yasserhessein/thyroid-disease-data-set, [Accessed: 25 May 2025].
  34. Y. Sun, J. Zhang, and Y. Zhang, “Adaboost algorithm combined multiple random forest models (Adaboost-RF) is employed for fluid prediction using well logging data,” Physics of Fluids, vol. 36, no. 1, 2024, [Online]. Available: http://dx.doi.org/10.1063/5.0179422.
  35. J. C. Ho, M. Sotoodeh, W. Zhang, R. L. Simpson, and V. S. Hertzberg, “An AdaBoost-based algorithm to detect hospital-acquired pressure injury in the presence of conflicting annotations,” Computers in Biology and Medicine, vol. 168, p. 107754, 2024, [Online]. Available: https://doi.org/10.1016/j.compbiomed.2023.107754.
  36. A. Mahkamov, T. Jumayev, D. Tuhtanazarov, and A. Dadamuxamedov, “Using AdaBoost to improve the performance of simple classifiers,” in Artificial Intelligence, Blockchain, Computing and Security, CRC Press, 2024, pp. 755-760.
  37. S. Amaouche, C. Hazman, A. Guezzaz, S. Benkirane, and M. Azrour, “Intrusion detection framework using AdaBoost algorithm and Chi-squared technique,” in Blockchain and Machine Learning for IoT Security, Chapman and Hall/CRC, 2024, pp. 92-111.
  38. G. M. Salama, A. Mohamed, and M. K. Abd-Ellah, “COVID-19 classification based on a deep learning and machine learning fusion technique using chest CT images,” Neural Computing and Applications, pp. 1-19, 2023, [Online]. Available: https://doi.org/10.1007/s00521-023-09346-7.
  39. S. K. Gharghan, R. Nordin, M. Ismail, and J. Abd Ali, “Accurate wireless sensor localization technique based on hybrid PSO-ANN algorithm for indoor and outdoor track cycling,” IEEE Sensors Journal, vol. 16, no. 2, pp. 529-541, 2015, [Online]. Available: https://doi.org/10.1109/JSEN.2015.2483745.
  40. Y. A. Ali, E. M. Awwad, M. Al-Razgan, and A. Maarouf, “Hyperparameter search for machine learning algorithms for optimizing the computational complexity,” Processes, vol. 11, no. 2, p. 349, 2023, [Online]. Available: https://doi.org/10.3390/pr11020349.
  41. A. H. M. Alaidi, Z. A. Ramadhan, J. S. Alrubaye, H. T. S. Alrikabi, H. A. Mutar, and I. Svyd, “AI-based monkeypox detection model using Raspberry Pi 5 AI Kit,” Sustainable Engineering and Innovation, vol. 7, no. 1, pp. 1-14, 2025.
  42. H. A. Hashim, S. L. Mohammed, and S. K. Gharghan, “Path loss model-based PSO for accurate distance estimation in indoor environments,” Journal of Communications, vol. 13, no. 12, pp. 712-722, 2018, [Online]. Available: https://doi.org/10.1007/s44196-023-00388-2.


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