Thyroid Disease Prediction Using XGBoost Algorithms
DOI:
https://doi.org/10.13052/jmm1550-4646.18322Keywords:
Thyroid disease, classification algorithm, Hormone, Machine leaning, Disease predictionAbstract
Nowadays, thyroid disease is increasing rapidly all over the world. Significantly, one out of ten people is affected by the thyroid in India. In recent years, many researchers have done various research works on thyroid disease detection. Therefore, the early stage of thyroid disease prediction is difficult to protect and avoid the worst health condition. In this regard, the machine learning plays a crucial role to detect the disease accurately. We consider the UC Irvin knowledge discovery dataset. So, this paper proposes the XGBoost algorithm to predict thyroid disease accurately. The best features are selected using XGBoost function. The proposed XGBoost algorithm’s efficacy is compared to decision tree, logistic regression, k-Nearest Neighbor (kNN) methods. The performance of all four algorithms is compared and analyzed. It is observed that the accuracy of the XGBoost algorithm increases by 2% than the KNN algorithm.
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Jha, R., Bhattacharjee, V., and Mustafi, A. (2021). Increasing the Prediction Accuracy for Thyroid Disease: A Step Towards Better Health for Society. Wireless Personal Communications, 1–18.
Shahajalal, M., Rahman, M., Pranto, S., Ema, R. R., Islam, T., and Raihan, M. (2022). Utilization of Machine Learning Algorithms for Thyroid Disease Prediction. In International Conference on Innovative Computing and Communications (pp. 57-69). Springer, Singapore.
Rajeswari, C., Sathiyabhama, B., Devendiran, S., and Manivannan, K. (2014). Bearing fault diagnosis using wavelet packet transform, hybrid PSO and support vector machine. Procedia Engineering, 97, 1772–1783.
Hosseinzadeh, M., Ahmed, O. H., Ghafour, M. Y., Safara, F., Ali, S., Vo, B., and Chiang, H. S. (2021). A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things. The Journal of Supercomputing, 77(4), 3616–3637.
Rehman, H. A. U., Lin, C. Y., Mushtaq, Z., and Su, S. F. (2021). Performance Analysis of Machine Learning Algorithms for Thyroid Disease. Arabian Journal for Science and Engineering, 1–13.
Vadivu, P. S. (2021, June). Improved Ensemble Classification Method for Thyroid Disease Using Data Mining Technologies. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1–7). IEEE.
Vinodhini, V., Vishalakshi, A., Chandrika, G. N., Sankar, S., and Ramasubbareddy, S. (2022). Predicting Vasovagal Syncope for Paraplegia Patients Using Average Weighted Ensemble Technique. Journal of Mobile Multimedia, 135–162.
Revathi, T. K., Sathiyabhama, B., and Sankar, S. (2021). A Deep Learning based approach for Diagnosing Coronary Inflammation with Multi-Scale Coronary Response Dynamic Balloon Tracking (MSCAR-DBT) based artery Segmentation in Coronary Computed Tomography Angiography (CCTA). Annals of the Romanian Society for Cell Biology, 25(6), 4936–4948.
Revathi, T. K., Sathiyabhama, B., and Sankar, S. (2021). Diagnosing Cardio Vascular Disease (CVD) using Generative Adversarial Network (GAN) in Retinal Fundus Images. Annals of the Romanian Society for Cell Biology, 2563–2572.
Vinodhini, V., Sathiyabhama, B., Sankar, S., and Somula, R. (2020). A Deep Structured Model for Video Captioning. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 12(2), 44–56.
Chaubey, G., Bisen, D., Arjaria, S., and Yadav, V. (2021). Thyroid disease prediction using machine learning approaches. National Academy Science Letters, 44(3), 233–238.
Temurtas, F. (2009). A comparative study on thyroid disease diagnosis using neural networks. Expert Systems with Applications, 36(1), 944–949.
Begum, A., and Parkavi, A. (2019, March). Prediction of thyroid disease using data mining techniques. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 342–345). IEEE.
Tyagi, A., Mehra, R., and Saxena, A. (2018, December). Interactive thyroid disease prediction system using machine learning technique. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 689–693). IEEE.
Asif, M. A. A. R., Nishat, M. M., Faisal, F., Shikder, M. F., Udoy, M. H., Dip, R. R., and Ahsan, R. (2020, December). 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.
Sonuç, E. (2021, July). Thyroid Disease Classification Using Machine Learning Algorithms. In Journal of Physics: Conference Series (Vol. 1963, No. 1, p. 012140). IOP Publishing.
Abbad Ur Rehman, H., Lin, C. Y., and Mushtaq, Z. (2021). Effective K-Nearest Neighbor Algorithms Performance Analysis of Thyroid Disease. Journal of the Chinese Institute of Engineers, 44(1), 77–87.
Riajuliislam, M., Rahim, K. Z., and Mahmud, A. (2021, February). Prediction of Thyroid Disease (Hypothyroid) in Early Stage Using Feature Selection and Classification Techniques. In 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD) (pp. 60–64). IEEE.
Nusinovici, S., Tham, Y. C., Yan, M. Y. C., Ting, D. S. W., Li, J., Sabanayagam, C., … and Cheng, C. Y. (2020). Logistic regression was as good as machine learning for predicting major chronic diseases. Journal of clinical epidemiology, 122, 56–69.
Charbuty, B., and Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20–28.
Tjahjadi, H., and Ramli, K. (2020). Noninvasive blood pressure classification based on photoplethysmography using k-nearest neighbors algorithm: A feasibility study. Information, 11(2), 93.
https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease
Chen, T., Guestrin, C., 2016. XGBoost: A scalable tree boosting system. In: Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Aug. 2016, pp. 785–794.
