Volume 5 , Issue 2 , PP: 01–12, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Murodbek Ahrorov 1 * , Ahmed Aziz 2
Doi: https://doi.org/10.54216/NIF.050201
Post-treatment follow-up in differentiated thyroid cancer requires a decision model that is not limited to binary recurrence prediction. Patients may present with partially reassuring anatomical findings, incomplete biochemical response, hetero-geneous pathological subtype, or contradictory clinical history. These situations are better described as a triadic state composed of support for recurrence, support against recurrence, and unresolved indeterminacy. This paper proposes a recurrence shadow mapping model based on single-valued neutrosophic clinical evidence. The model transforms clinico-pathologic descriptors into truth, indeterminacy, and falsity memberships; aggregates evidence through entropy-contrast weighting; and produces a recurrence-shadow index that separates stable, observation, alert, and high-alert follow-up states. The proposed method is designed for healthcare decision support rather than automatic replacement of clinical judgment. Its mathematical contribution is a bounded neutrosophic score that penalizes inconsistent evidence without suppressing clinically meaningful warning signals. Experimental evaluation demonstrates that recurrence-oriented evidence sources can be expressed in a transparent mathematical form, and that indeterminacy itself becomes an interpretable clinical quantity. The findings support the use of neutrosophic information fusion for medical cases where uncertainty is structural rather than merely statistical.
Neutrosophic healthcare decision support , Recurrence shadow , Differentiated thyroid cancer , Singlevalued neutrosophic set , Clinical evidence fusion , Indeterminacy-aware risk stratification
[1] Ahmad, M. A.-S. and Haddad, J. (2024). An explainable ai model for predicting the recurrence of differentiated thyroid cancer. arXiv preprint arXiv:2410.10907.
[2] Ali, S., Rahim, M., Bajri, S. A., Ahmad, S., Alharbi, R., and Khalifa, H. A. E.-W. (2024). alpha, beta, gammaneutrosophic aggregation operators and their applications in the software site selection. Heliyon, 10(10):e31417.
[3] Borzooei, S., Briganti, G., Golparian, M., Lechien, J. R., and Tarokhian, A. (2024). Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study. European Archives of Oto-Rhino-Laryngology, 281:2095–2104.
[4] Borzooei, S. and Tarokhian, A. (2023). Differentiated thyroid cancer recurrence. UCI Machine Learning Repository.
[5] Clark, E., Price, S., Lucena, T., Haberlein, B., Wahbeh, A., and Seetan, R. (2024). Predictive analytics for thyroid cancer recurrence: A machine learning approach. Knowledge, 4(4):557–570.
[6] Farid, H. M. A. and Riaz, M. (2022). Single-valued neutrosophic einstein interactive aggregation operators with applications for material selection in engineering design: case study of cryogenic storage tank. Complex & Intelligent Systems.
[7] Farid, H. M. A. and Riaz, M. (2023). Single-valued neutrosophic dynamic aggregation operators for multi-period decision making. Engineering Applications of Artificial Intelligence, 123:106940.
[8] Habchi, Y., Himeur, Y., Kheddar, H., Boukabou, A., Atalla, S., Chouchane, A., Ouamane, A., and Mansoor,W. (2023). Ai in thyroid cancer diagnosis: Techniques, trends, and future directions. arXiv preprint arXiv:2308.13592.
[9] Haddad, R. I., Bischoff, L., Ball, D., et al. (2022). Thyroid carcinoma, version 2.2022, nccn clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network, 20(8):925–951.
[10] Irvanizam, I. (2024). An improved rafsi method based on single-valued trapezoidal neutrosophic number for healthcare service quality evaluation. Expert Systems with Applications, page 123343.
[11] Latif, S. et al. (2024). Decision support system for single-valued neutrosophic information. Mathematical Problems in Engineering, page 4362151.
[12] Lee, T. F., Lee, S. H., Tseng, C. D., et al. (2023). Using machine learning algorithm to analyse the hypothyroidism complications caused by radiotherapy in patients with head and neck cancer. Scientific Reports, 13:19185.
[13] Liu, P. et al. (2022). Novel edas methodology based on single-valued neutrosophic aczel-alsina aggregation information. Mathematical Problems in Engineering, page 2394472.
[14] Loor-Torres, R., Wu, Y., Cabezas, E., Borras, M., Toro-Tobon, D., Duran, M., et al. (2024). Use of natural language processing to extract and classify papillary thyroid cancer features from surgical pathology reports. arXiv preprint arXiv:2406.00015.
