References
- Ahmad, W., Ahmad, A., Lu, C., A Novel Hybrid Decision Support System for Thyroid Disease Forecasting, Soft Computing, 22, 2018, 5377–5383.
- Aldughayfiq, B., Ashfaq, F., Jhanjhi, N. Z., Humayun, M., Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP, Diagnostics, 13, 2023, 1932, https://doi.org/10.3390/diagnostics13111932.
- Allgaier, J., Mulansky, L., Draelos, R. L., Pryss, R., How Does the Model Make Predictions? A Systematic Literature Review on the Explainability Power of Machine Learning in Healthcare, Artificial Intelligence in Medicine, 143, 2023, 102616, https://doi.org/10.1016/j.artmed.2023.102616.
- Amin, A., Hasan, K., Zein-Sabatto, S., Chimba, D., Ahmed, I., Islam, T., An Explainable AI Framework for Artificial Intelligence of Medical Things, Proc. IEEE Globecom Workshops (GC Wkshps), 2023, 2097–2102.
- Chaganti, R., Rustam, F., De La Torre Díez, I., V. Mazon, J. L., Rodríguez, C. L., Ashraf, I., Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques, Cancers, 14, 2022.
- du Prel, J. B., Hommel, G., Röhrig, B., Blettner, M., Confidence Interval or P-Value? Part 4 of a Series on Evaluation of Scientific Publications, Deutsches Ärzteblatt International, 2009, https://doi.org/10.3238/arztebl.2009.0335.
- Ghosh, S. K., Khandoker, A. H., Investigation on Explainable Machine Learning Models to Predict Chronic Kidney Diseases, Scientific Reports, 14, 2024, 3687.
- Guler, H., Avcı, D., Ula¸s, M., Omma, T., Performance Comparison of Machine Learning Models Powered by SHAP and LIME Based Explainability Techniques on Diabetes Dataset, SSRN, 2024, https://ssrn.com/abstract=4713039.
- Guidotti, R., Monreale, A., Giannotti, F., Pedreschi, D., Ruggieri, S., Turini, F., Factual and Counterfactual Explanations for Black Box Decision Making, IEEE Intelligent Systems, 34, 6, 2019, 14–23.
- Hakkoum, H., Idri, A., Abnane, I., Assessing and Comparing Interpretability Techniques for Artificial Neural Networks in Breast Cancer Classification, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 2021, https://doi.org/10.1080/21681163.2021.1901784.
- Hu, Y., Sokolova, M., Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data, arXiv preprint, 2021, arXiv:2113.13430.
- Junkang, A., Zhang, Y., Joe, I., Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models, Applied Sciences, 13, 15, 2023, 8782.
- Kaggle, Thyroid Disease Dataset, https://www.kaggle.com/datasets/emmanuelfwerr/thyroid-diseasedata.
- Mulwa, M. M., Mwangi, R. W., Mindila, A., GMM-LIME Explainable Machine Learning Model for Interpreting Sensor-Based Human Gait, Engineering Reports, 2024.
- Ouartani, S., Taleb, N., Decision Support System for Thyroid Disease Prediction Using Decision Tree Algorithm and Ontology, Artificial Intelligence Theory and Applications (AITA), 2024.
- Rahman, M., Ali, M., Mahim, M., Miah, S. M., Sipon, M., Enhancing Lung Abnormalities Detection and Classification Using a Deep CNN and GRU with Explainable AI, Machine Learning with Applications, 14, 2023, 100492, https://doi.org/10.1016/j.mlwa.2023.100492.
- Rawte, V., Royl, B., V. L., Thyroid Disease Diagnosis using Ontology-based Expert System, International Journal of Engineering Research Technology (IJERT), 4, 6, 2015.
- Settouti, N., Saidi, M., Preliminary Analysis of Explainable Machine Learning Methods for Multiple Myeloma Chemotherapy Treatment Recognition, Evolutionary Intelligence, 17, 2024, 513–533.
- Shiuh, T. L., Khai, W. K., Xin, Y. C., Wai, C. Y., Prediction of Thyroid Disease Using Machine Learning Approaches and Featurewiz Selection, JTEC, 15, 3, 2023, 9–16.
- Siddhartha, K., Abhishek, A., Gyanendra, S., Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model, International Journal of Business Analytics (IJBAN), 9, 3, 2022, 1–18.
- Sun, Q., Akman, A., Schuller, B. W., Explainable Artificial Intelligence for Medical Applications: A Review, arXiv preprint, 2024, arXiv:2412.01829.
- Tang, J., Alelyani, S., Liu, H., Feature Selection for Classification: A Review, in: C. C. Aggarwal (Ed.), Data Classification: Algorithms and Applications, Chapman and Hall/CRC, 2014, 37–64.
- Zacharias, J., von Zahn, M., Chen, J., Designing a Feature Selection Method Based on Explainable Artificial Intelligence, Electronic Markets, 2022, https://doi.org/10.1007/s12525-022-00608-1.