Abstract
This article compares various classification techniques in their ability to predict risk levels and recurrence in patients with thyroid disorders. Focusing on a detailed dataset incorporating clinical and pathological features, four prominent classification methods were implemented and evaluated: Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM). The findings revealed that Random Forests achieved the highest accuracy in predicting the risk level (88.70%) and the determination of disease recurrence (96.52%), outperforming the other evaluated techniques. Logistic Regression and Decision Trees also demonstrated solid performance, with accuracies exceeding 80% for both target variables, while SVM exhibited comparatively lower performance. This analysis highlights the importance of carefully selecting classification algorithms based on specific prediction objectives in medical studies, underscoring the effectiveness of Random Forests for this dataset. The research significantly contributes to the field of medical analytics, offering critical insights for the development of more accurate and efficient predictive models in the diagnosis and monitoring of thyroid disorders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Asif, M.A., et al.: Computer aided diagnosis of thyroid disease using machine learning algorithms. In: 2020 11th International Conference on Electrical and Computer Engineering (ICECE), pp. 222–225. IEEE (2020)
Dhanamithra, R.G., Kumar, R.S.: A comparison study on machine learning approaches for thyroid disease prediction. In: 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 1189–1192. IEEE (2022)
Verma, S., Popli, R., Kumar, H., Srivastava, A.: Classification of thyroid diseases using machine learning frameworks. Int. J. Health Sci. I, 7552–7566 (2022)
Shama, A., Hossain, M.B., Adhikary, A., Uddin, K.A., Hossain, M.A.: Prediction of hypothyroidism and hyperthyroidism using machine learning algorithms (2022)
Bhende, D., et al.: Machine learning-based classification of thyroid disease: a comprehensive study on early detection and risk factor analysis. In: 2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp. 1–6. IEEE (2024)
Kumar, R.R., Lakshmi, M.S., Ashwak, B.S., Rajeshwari, K., Zaid, S.M.: Thyroid disease classification using machine learning algorithm (2023)
Balasree, K., Dharmarajan, K.: Thyroid classification using deep learning techniques. In: 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), pp. 845–852. IEEE (2023)
Raju, K.B., Lakineni, P.K., Indrani, K.S., Latha, G.M.S., Saikumar, K.: Optimized building of machine learning technique for thyroid monitoring and analysis. In: 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), pp. 1–6. IEEE (2021)
Ramasamy, M.D., Periasamy, K., Krishnasamy, L., Dhanaraj, R.K., Kadry, S., Nam, Y.: Multi-disease classification model using strassen’s half of threshold (SHoT) training algorithm in healthcare sector. IEEE Access 9, 112624–112636 (2021)
Afshan, N., Mushtaq, Z., Alamri, F.S., Qureshi, M.F., Khan, N.A., Siddique, I.: Efficient thyroid disorder identification with weighted voting ensemble of super learners by using adaptive synthetic sampling technique. AIMS Math. 8(10), 24274–24309 (2023)
Peya, Z.J., Islam, M.S., Chumki, M.K.N.: Thyroid disease prediction based on feature selection and machine learning. In: 2022 25th International Conference on Computer and Information Technology (ICCIT), pp. 495–500. IEEE (2022)
UCI Machine Learning Repository: Thyroid Disease Dataset. https://archive.ics.uci.edu/dataset/102/thyroid+disease. Accessed 14 Apr 2024
UCI Machine Learning Repository. (n.d.). Differentiated Thyroid Cancer Recurrence Dataset. https://archive.ics.uci.edu/dataset/915/differentiated+thyroid+cancer+recurrence. Accesed 30 May 2024
Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M., De la Hoz-Franco, E., Echeverri-Ocampo, I., Salas-Navarro, K.: Parkinson disease analysis using supervised and unsupervised techniques. In: Tan, Y., Shi, Y., Niu, B. (eds.) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol. 11656, pp. 191−199. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26354-6_19
Patiño-Saucedo, J.A., et al.: Predictive model for human activity recognition based on machine learning and feature selection techniques. Int. J. Environ. Res. Public Health 19(19), 12272 (2022)
Mammone, A., Turchi, M., Cristianini, N.: Support vector machines. Wiley Interdis. Rev. Comput. Stat. 1(3), 283–289 (2009)
Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. Int. J. Neural Syst. 19(04), 295–308 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ariza-Colpas, P., Piñeres-Melo, M., Barceló-Martínez, En., Vidal-Merlano, D., Barceló-Castellanos, C., Roman-Fabian (2024). Comparative Evaluation of Classification Techniques for Predicting Risk and Recurrene of Thyroid Disorders. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2024. Lecture Notes in Computer Science, vol 14789. Springer, Singapore. https://doi.org/10.1007/978-981-97-7184-4_25
Download citation
DOI: https://doi.org/10.1007/978-981-97-7184-4_25
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-7183-7
Online ISBN: 978-981-97-7184-4
eBook Packages: Computer ScienceComputer Science (R0)