Comparative Analysis of Tree-Based Machine Learning Algorithms on Thyroid Disease Prediction Using ROS Technique and Hyperparameter Optimization | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 4، بهمن 2024، صفحه 511-520 اصل مقاله (1.13 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2025.14990.2598 | ||
| نویسنده | ||
| Elahe Moradi* | ||
| Department of Electrical and Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran. | ||
| چکیده | ||
| Thyroid disease is common worldwide and early diagnosis plays an important role in effective treatment and management. Utilizing machine learning techniques is vital in thyroid disease diagnosis. This research proposes tree-based machine learning algorithms using hyperparameter optimization techniques to predict thyroid disease. The thyroid disease dataset from the UCI Repository is benchmarked to evaluate the performance of the proposed algorithms. After data preprocessing and normalization steps, data balancing has been applied to the data using the random oversampling (ROS) technique. Also, two methods of grid search (GS) and random search (RS) have been employed to optimize hyperparameters. Finally, employing Python software, various criteria were used to evaluate the performance of proposed algorithms such as decision tree, random forest, AdaBoost, and extreme gradient boosting. The results of the simulations indicate that the Extreme Gradient Boosting (XGB) algorithm with the grid search method outperforms all the other algorithms, obtaining an impressive accuracy, AUC, sensitivity, precision, and MCC of 99.39%, 99.97%, 98.85%, 99.40%, 98.79%, respectively. These results demonstrated the potential of the proposed method for accurately predicting thyroid disease. | ||
| کلیدواژهها | ||
| Machine Learning؛ Thyroid Disease Prediction؛ Imbalanced Data؛ Optimization؛ Random Forest | ||
| مراجع | ||
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[1] K. Geetha and S. Baboo, “An Empirical Model for Thyroid Disease Classification using Evolutionary Multivariate Bayseian Prediction Method,” Global Journal of Computer Science and Technology, vol. 16, no. 1, pp. 1-10, 2016.
[2] M. Pal, S. Parija, and G. Panda, “Enhanced Prediction of Thyroid Disease Using Machine Learning Method,” IEEE VLSI Device Circuit and System, pp. 199-204, Feb. 2022.
[3] R. H. Agarwal, S. Degadwala, and D. Vyas, “Predictive Modeling for Thyroid Disease Diagnosis using Machine Learning,” International Conference on Inventive Computation Technologies (ICICT), pp.227-231, Apr. 2024.
[4] N. Jothi, N. A. Rashid, and W. Husain, “Data Mining in Healthcare – A Review,” Procedia Computer Science, vol. 72, pp. 306–313, Jan. 2015.
[5] D. Asif, M. Bibi, M. S. Arif, and A. Mukheimer, “Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization,” Algorithms, vol. 16, no. 6, p. 308, Jun. 2023.
[6] M. Ramya and P. V. S. Kumar, “PREDICTION AND PROVIDING MEDICATION FOR THYROID DISEASE USING MACHINE LEARNING TECHNIQUE (SVM),” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 11, no. 3, pp. 1099–1107, Jan. 2020.
[7] V. Prasad, T. S. Rao, and M. S. Babu, “Thyroid disease diagnosis via hybrid architecture composing rough data sets theory and machine learning algorithms,” Soft Computing, vol. 20, no. 3, pp. 1179–1189, Jan. 2015.
[8] T. Khan, “Application of Two-Class Neural of Thyroid Disease,”, 11th International Conference on Cloud Computing, Data Science & Engineering, Jan. 2021.
[9] P. Kumari et al., “Explainable artificial intelligence and machine learning algorithms for classification of thyroid disease,” Deleted Journal, vol. 6, no. 7, Jul. 2024.
[10] R. Chaganti, F. Rustam, I. De La Torre Díez, J. L. V. Mazón, C. L. Rodríguez, and I. Ashraf, “Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques,” Cancers, vol. 14, no. 16, p. 3914, Aug. 2022.
[11] S. S. Islam, Md. 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, Mar. 2022.
[12] M. Hosseinzadeh et al., “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, vol. 77, no. 4, pp. 3616–3637, Aug. 2020.
[13] M. H. Alshayeji, “Early Thyroid Risk Prediction by Data Mining and Ensemble Classifiers,” Machine Learning and Knowledge Extraction, vol. 5, no. 3, pp. 1195–1213, Sep. 2023.
[14] S. Razia, P. SwathiPrathyusha, N. V. Krishna, and N. S. Sumana, “A Comparative study of machine learning algorithms on thyroid disease prediction,” International Journal of Engineering & Technology, vol. 7, no. 2.8, p. 315, Mar. 2018.
[15] G. Mollica et al., “Classification of Thyroid Diseases Using Machine Learning and Bayesian Graph Algorithms,” IFAC-PapersOnLine, vol. 55, no. 40, pp. 67–72, Jan. 2022.
[16] S. Dalal et al., “Enhancing thyroid disease prediction with improved XGBoost model and bias management techniques,” Multimedia Tools and Applications, Jul. 2024.
[17] P. Poudel, A. Illanes, E. J. G. Ataide, N. Esmaeili, S. Balakrishnan, and M. Friebe, “Thyroid Ultrasound Texture Classification Using Autoregressive Features in Conjunction with Machine Learning Approaches,” IEEE Access, vol. 7, pp. 79354–79365, Jan. 2019.
[18] W. Song et al., “Multitask Cascade Convolution Neural Networks for Automatic Thyroid Nodule Detection and Recognition,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, pp. 1215–1224, May 2019.
[19] J. A. Chandio, G. A. Mallah, and N. A. Shaikh, “Decision Support System for Classification Medullary Thyroid Cancer,” IEEE Access, vol. 8, pp. 145216–145226, Jan. 2020.
[20] X. Zhao et al., “Automatic Thyroid Ultrasound Image Classification Using Feature Fusion Network,” IEEE Access, vol. 10, pp. 27917–27924, Jan. 2022.
[21] E. Moradi, “A Data-Driven based Robust Multilayer Perceptron Approach for Fault Diagnosis of Power Transformers,” 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), Feb. 2024.
[22] F. Kamalov, H.-H. Leung, and A. K. Cherukuri, “Keep it simple: random oversampling for imbalanced data,” Advances in Science and Engineering Technology International Conferences (ASET), Feb. 2023.
[23] L. Aversano et al., “Thyroid Disease Treatment prediction with machine learning approaches,” Procedia Computer Science, vol. 192, pp. 1031–1040, Jan. 2021.
[24] R. Jha, V. Bhattacharjee, and A. Mustafi, “Increasing the Prediction Accuracy for Thyroid Disease: A Step Towards Better Health for Society,” Wireless Personal Communications, vol. 122, no. 2, pp. 1921–1938, Aug. 2021.
[25] T. R. Mahesh et al., “AdaBoost Ensemble Methods Using K-Fold Cross Validation for Survivability with the Early Detection of Heart Disease,” Computational Intelligence and Neuroscience, vol. 2022, pp. 1–11, Apr. 2022.
[26] Y. Chen, D. Li, X. Zhang, J. Jin, and Y. Shen, “Computer aided diagnosis of thyroid nodules based on the devised small-datasets multi-view ensemble learning,” Medical Image Analysis, vol. 67, Jan. 2021.
[27] K. Sumwiza, C. Twizere, G. Rushingabigwi, P. Bakunzibake, and P. Bamurigire, “Enhanced cardiovascular disease prediction model using random forest algorithm,” Informatics in Medicine Unlocked, vol. 41, Jan. 2023
[28] P. T. Noi and M. Kappas, “Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery,” Sensors, vol. 18, no. 2, Dec. 2017.
[29] T. Agrawal, Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient. Apress: Berkeley, CA, USA, pp. 31–51, 2021.
Agrawal, T.; Agrawal, T. Hyperparameter optimization using scikit-learn. In Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning models More Efficient; Apress: Berkeley, CA, USA, 2021; pp. 31–51.
[30] J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” vol. 13, no. 1, pp. 281–305, Mar. 2012.
[31] S. M. Rostami and M. Ahmadzadeh, “Extracting Predictor Variables to Construct Breast Cancer Survivability Model with Class Imbalance Problem,” Journal of AI and Data Mining, vol. 6, no. 2, pp. 263-276, 2018.
[32] D. Chicco and G. Jurman, “The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification,” BioData Mining, vol. 16, no. 1, Feb. 2023. | ||
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