CFP last date
16 December 2024
Call for Paper
January Edition
IJAIS solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 16 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

Development of Thyroid Disease Prediction Model in Nigeria

by Terlumun Emmanuel Togor, Joshua Abah, Dekera Kenneth Kwaghtyo
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 41
Year of Publication: 2023
Authors: Terlumun Emmanuel Togor, Joshua Abah, Dekera Kenneth Kwaghtyo
10.5120/ijais2023451950

Terlumun Emmanuel Togor, Joshua Abah, Dekera Kenneth Kwaghtyo . Development of Thyroid Disease Prediction Model in Nigeria. International Journal of Applied Information Systems. 12, 41 ( Sep 2023), 33-47. DOI=10.5120/ijais2023451950

@article{ 10.5120/ijais2023451950,
author = { Terlumun Emmanuel Togor, Joshua Abah, Dekera Kenneth Kwaghtyo },
title = { Development of Thyroid Disease Prediction Model in Nigeria },
journal = { International Journal of Applied Information Systems },
issue_date = { Sep 2023 },
volume = { 12 },
number = { 41 },
month = { Sep },
year = { 2023 },
issn = { 2249-0868 },
pages = { 33-47 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number41/development-of-thyroid-disease-prediction-model-in-nigeria/ },
doi = { 10.5120/ijais2023451950 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-14T17:32:45+05:30
%A Terlumun Emmanuel Togor
%A Joshua Abah
%A Dekera Kenneth Kwaghtyo
%T Development of Thyroid Disease Prediction Model in Nigeria
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 41
%P 33-47
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Thyroid Diseases are often diagnosed in Nigeria's endocrinology clinics and are reported as the second most common endocrine disease encountered amongst Nigerians, especially women. This led to a vast volume of thyroid data. When applied to machine learning algorithms, the data can greatly benefit endocrinologists, patients, and health organizations. However, diverse machine-learning methods have been in place for thyroid disease prediction leveraging the UCI thyroid data for experimental purposes. Conversely, this study differs by utilizing indigenous thyroid data to predict the thyroid conditions: hypothyroidism, hyperthyroidism and euthyroidism. The dataset was pre-processed using pandas and NumPy libraries. Random Forest classifier and Support Vector Machine classifier were trained using the indigenous dataset. Experimentally, the model was evaluated using Accuracy, Precision, F1-measure, Sensitivity and the Receiver Operating Characteristic (ROC) curve – Area Under the Curve AUC. The classification results show that the Random Forest classifier obtained the best accuracy of 99.30%, while the Support Vector Machine classifier achieved an accuracy of 98.60%. The study achieved the goal of turning the data comprising of thyroid conditions gathered from the Federal Medical Centre, Yenagoa, Bayelsa State, Nigeria into a powerful tool to support endocrinologists, patients, and health organizations.

References
  1. A. O. Ogbera, O. Fasanmade, and O. Adediran, “Pattern of Thyroid Disorders in the Southwestern Region of Nigeria,” Ethn. Dis., vol. 17, 2007.
  2. Anthonia and Sonny, “Epidemiology of thyroid diseases in Africa.”
  3. O.-O. N. Fidelis and A.-P. J., “A Multigene Genetic Programming Model for Thyroid Disorder Detection,” 2015.
  4. M. R. Obeidavi, A. L. I. Rafiee, and O. Mahdiyar, “Diagnosing Thyroid Disease by Neural Networks,” Biomed. Pharmacol. J., vol. 10, no. 2, pp. 509–524, 2017, doi: doi.org/10.13005/bpj/1137.
  5. I. Mofek and Z. Bozkuş, “Use of Machine Learning Techniques for Diagnosis of Thyroid Gland Disorder,” 2016.
  6. S. Godara and S. Kumar, “Prediction of Thyroid Disease Using Machine Learning Techniques,” Int. J. Electron. Eng. (ISSN, vol. 10, no. 2, pp. 787–793, 2018, [Online]. Available: www.csjournals.com%0APrediction
  7. A. Tyagi, R. Mehra, and A. Saxena, “Interactive Thyroid Disease Prediction System Using Machine Learning Technique,” 2018 Fifth Int. Conf. Parallel, Distrib. Grid Comput., no. March, pp. 689–693, 2019, doi: 10.1109/PDGC.2018.8745910.
  8. D. S. Reddy, O. S. Vaishnavi, J. Vidya, K. S. Sharan, and R. Subramanyam, “Literature Survey,” Int. J. Adv. Sci. Technol., vol. 29, no. 5, pp. 4752–4761, 2020.
  9. R. Chandan, C. Vasan, M. S. Chethan, and H. S. Devikarani, “Thyroid Detection using Machine Learning,” Int. J. Eng. Appl. Sci. Technol., vol. 5, no. 9, pp. 173–177, 2021, [Online]. Available: http://www.ijeast.com
  10. A. Shama, ( B. H., A. Adhikary, A. Uddin, and M. A. Hossain, “Prediction of Hypothyroidism and Hyperthyroidism Using Machine Learning Algorithms,” Res. Sq., pp. 1–21, 2022, [Online]. Available: doi.org/10.21203/rs.3.rs-1486798/v2%0ALicense:
  11. N. A. Sajadi, S. Borzouei, H. Mahjub, and M. Farhadian, “Diagnosis of hypothyroidism using a fuzzy rule-based expert system,” Clin. Epidemiol. Glob. Heal., vol. 7, no. 4, pp. 519–524, 2019, doi: 10.1016/j.cegh.2018.11.007.
  12. L. Shalini and M. R. Ghalib, “A Hypothyroidism Prediction using Supervised Algorithm,” no. 1, pp. 7285–7288, 2019, doi: 10.35940/ijeat.F7897.109119.
  13. K. Dharmarajan, K. Balasree, A. S. Arunachalam, and K. Abirmai, “Thyroid Disease Classification Using Decision Tree and SVM,” Indian J. Public Heal. Res. Dev., vol. 11, no. 03, pp. 224–229, 2020.
  14. M. Mourad et al., “Machine Learning and Feature Selection Applied to SEER Data to Reliably Assess Thyroid Cancer Prognosis,” Sci. Rep., pp. 1–11, 2020, doi: 10.1038/s41598-020-62023-w.
  15. S. Borzouei, H. Mahjub, N. A. Sajadi, and M. Farhadian, “Diagnosing thyroid disorders: Comparison of logistic regression and neural network models,” J. Fam. Med. Prim. Care, 2020, doi: 10.4103/jfmpc.jfmpc.
  16. K. A. Salman and E. Sonuc, “The Efficiency of Classification Techniques in Predicting Thyroid Disease,” 2021.
  17. V. V. Vadhiraj, A. Simpkin, J. O. Connell, N. S. Ospina, S. Maraka, and D. T. O. Keeffe, “Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques,” Medicina (B. Aires)., vol. 57, no. 527, pp. 1–18, 2021, doi: doi.org/10.3390/medicina57060527.
  18. L. Aversano et al., “Thyroid Disease Treatment prediction with machine learning approaches,” Procedia Comput. Sci., vol. 192, pp. 1031–1040, 2021, doi: 10.1016/j.procs.2021.08.106.
  19. T. Singh, A. K. Sahu, S. D. Greater, M. P. Sharma, S. Verma, and C. Kumar, “Treatment of thyroid disease through machine learning predictive model,” Int. J. Heal. Sci. ISSN, vol. 6, no. July, pp. 3176–3188, 2022, doi: doi.org/10.53730/ijhs.v6nS8.12813 Treatment.
  20. S. S. Islam, S. Haque, M. S. U. Miah, T. Bin, and R. Nugraha, “Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study,” PeerJ Comput. Sci., pp. 1–35, 2022, doi: 10.7717/peerj-cs.898.
  21. M. Ramya and P. V. S. Kumar, “Prediction and Providing Medication for Thyroid Disease using Machine Learning Technique (SVM),” Turkish J. Comput. Math. Educ., vol. 11, pp. 1099–1107, 2020.
  22. R. Bridgelall, "Tutorial on Support Vector Machines," no. February 2022, doi 10.20944/preprints202201. 0232.v1.
  23. O. Mbaabu and Onesmus, “Introduction to Random Forest in Machine Learning,” pp. 1–20, 2020.
  24. N. Donges, “What Is Random Forest_ A Complete Guide _ Built In.”
Index Terms

Computer Science
Information Sciences
Thyroid-disease prediction
Nigeria endocrinological data

Keywords

Thyroid disease Hypothyroidism Hyperthyroidism Euthyroidism SVM Random-Forest Endocrinology

Powered by PhDFocusTM