Supporting Medical Diagnostic Reasoning via a Dissimilarity Measure for Single-Valued Neutrosophic Sets
DOI:
https://doi.org/10.65069/jessd21202621Keywords:
Single-valued neutrosophic sets, Dissimilarity measure, Medical diagnosisAbstract
Medical diagnosis is often carried out under incomplete, imprecise, or even conflicting clinical information. Single-valued neutrosophic sets are useful for describing this type of uncertainty, but the final diagnostic result can still depend strongly on how patient profiles are compared with disease patterns. Some available measures do not provide sufficient discrimination when the underlying component variations are different. To address this issue, this study develops a bounded dissimilarity measure for single-valued neutrosophic sets and examines its application to medical diagnosis. For each criterion, the measure first takes the minimum absolute difference among the truth, indeterminacy, and falsity components. A bounded nonlinear function is then applied, and the resulting values are aggregated across all criteria. The main properties of the measure are established theoretically, while its ability to distinguish different profiles is further examined through numerical examples and comparisons with eight existing measures. The numerical results show that the proposed measure can distinguish several cases that receive identical values under some existing approaches. Two medical examples are also considered. The minimum-dissimilarity rule identifies malaria in one case and bacterial pneumonia in the respiratory case. For the latter, the resulting ranking is consistent with those obtained using the comparison measures, while the proposed measure provides greater separation between the most plausible disease and the other candidates. Overall, the measure appears suitable for ranking uncertain medical profiles, although further testing with larger clinical datasets is needed.
References
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Smarandache, F. (1998). Neutrosophy: Neutrosophic probability, set, and logic. American Research Press.
Wang, H., Smarandache, F., Zhang, Y.-Q., & Sunderraman, R. (2010). Single valued neutrosophic sets. Multispace and Multistructure, 4, 410–413.
Liu, Z., & Zhu, S. (2025). A generalized Hellinger distance-based spherical fuzzy TOPSIS method for sustainable transport systems selection. In Intelligent urban mobility (pp. 211–228). Elsevier. https://doi.org/10.1016/B978-0-443-34160-1.00002-X
Liu, Z., Wang, D., Deveci, M., & Letchmunan, S. (2025). Interval-valued Pythagorean fuzzy distance-based extended inferior ratio method for multiattribute decision-making: Application to green supplier selection in manufacturing industry. Applied Soft Computing, 113935. https://doi.org/10.1016/j.asoc.2025.113935
Kumar, R., & Kumar, S. (2024). An extended combined compromise solution framework based on novel intuitionistic fuzzy distance measure and score function with applications in sustainable biomass crop selection. Expert Systems with Applications, 239, 122345. https://doi.org/10.1016/j.eswa.2023.122345
Garg, H., Dutta, D., Dutta, P., & Gohain, B. (2024). An extended group decision-making algorithm with intuitionistic fuzzy set information distance measures and their applications. Computers & Industrial Engineering, 197, 110537. https://doi.org/10.1016/j.cie.2024.110537
Abraham, C. O., Ejegwa, P. A., Kausar, N., Akwu, A. D., & Cagin, T. (2026). An enhancement of smartphone selection process based on a new 3D Pythagorean fuzzy distance metric via TOPSIS technique. Intelligent Data Analysis, 30(4), 944–971. https://doi.org/10.1177/1088467X251387621
Garg, H., & Nancy. (2017). Some new biparametric distance measures on single-valued neutrosophic sets with applications to pattern recognition and medical diagnosis. Information, 8(4), 162. https://doi.org/10.3390/info8040162
Chai, J. S., Selvachandran, G., Smarandache, F., Gerogiannis, V. C., Son, L. H., Bui, Q.-T., & Vo, B. (2021). New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems. Complex & Intelligent Systems, 7(2), 703–723. https://doi.org/10.1007/s40747-020-00220-w
Zeng, Y., Ren, H., Yang, T., Xiao, S., & Xiong, N. (2022). A novel similarity measure of single-valued neutrosophic sets based on modified Manhattan distance and its applications. Electronics, 11(6), 941. https://doi.org/10.3390/electronics11060941
Singh, S., & Sharma, S. (2024). Aggregation operators-based divergence measures for single-valued neutrosophic sets with their applications to pattern recognition. Journal of Intelligent & Fuzzy Systems, 46(4), 9007–9020. https://doi.org/10.3233/JIFS-232369
Bisht, G., & Pal, A. K. (2025). A novel similarity measure for single-valued neutrosophic sets based on the inner product and its applications in pattern recognition and medical diagnosis. Pattern Analysis and Applications, 28(3), 143. https://doi.org/10.1007/s10044-025-01445-6
Ye, J. (2015). Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artificial Intelligence in Medicine, 63(3), 171–179. https://doi.org/10.1016/j.artmed.2014.12.007
Shahzadi, G., Akram, M., & Saeid, A. B. (2017). An application of single-valued neutrosophic sets in medical diagnosis. Neutrosophic Sets and Systems, 18, 80–88. https://doi.org/10.5281/zenodo.1175158
Ye, J., & Fu, J. (2016). Multi-period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function. Computer Methods and Programs in Biomedicine, 123, 142–149. https://doi.org/10.1016/j.cmpb.2015.10.002
Zhang, C., Li, D., Broumi, S., & Sangaiah, A. K. (2018). Medical diagnosis based on single-valued neutrosophic probabilistic rough multisets over two universes. Symmetry, 10(6), 213. https://doi.org/10.3390/sym10060213
Xiao, F., & Ding, W. (2019). Divergence measure of Pythagorean fuzzy sets and its application in medical diagnosis. Applied Soft Computing, 79, 254–267. https://doi.org/10.1016/j.asoc.2019.03.043
Broumi, S., & Smarandache, F. (2013). Several similarity measures of neutrosophic sets. Neutrosophic Sets and Systems, 1, 54–62. https://doi.org/10.5281/zenodo.30312
Majumdar, P., & Samanta, S. K. (2014). On similarity and entropy of neutrosophic sets. Journal of Intelligent & Fuzzy Systems, 26(3), 1245–1252. https://doi.org/10.3233/IFS-130810
Huang, H.-L. (2016). New distance measure of single-valued neutrosophic sets and its application. International Journal of Intelligent Systems, 31(10), 1021–1032. https://doi.org/10.1002/int.21815
Liu, C., & Luo, Y. (2016). The weighted distance measure based method to neutrosophic multi-attribute group decision making. Mathematical Problems in Engineering, 2016, Article 3145341. https://doi.org/10.1155/2016/3145341
Ren, H., Xiao, S., & Zhou, H. (2019). A chi-square distance-based similarity measure of single-valued neutrosophic set and applications. International Journal of Computers Communications & Control, 14(1), 78–89. https://doi.org/10.15837/ijccc.2019.1.3430
Ye, J. (2017). Single-valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine. Soft Computing, 21(3), 817–825. https://doi.org/10.1007/s00500-015-1818-y
Mondal, K., Pramanik, S., & Giri, B. C. (2018). Hybrid binary logarithm similarity measure for MAGDM problems under SVNS assessments. Neutrosophic Sets and Systems, 20, 12–25. https://doi.org/10.5281/zenodo.1235365
Zhang, E., Chen, F., & Zeng, S. (2020). Integrated weighted distance measure for single-valued neutrosophic linguistic sets and its application in supplier selection. Journal of Mathematics, 2020, Article 6468721. https://doi.org/10.1155/2020/6468721
Garg, H., & Nancy. (2020). Algorithms for single-valued neutrosophic decision making based on TOPSIS and clustering methods with new distance measure. AIMS Mathematics, 5(3), 2671–2693. https://doi.org/10.3934/math.2020173
Ali, M., Hussain, Z., & Yang, M.-S. (2023). Hausdorff distance and similarity measures for single-valued neutrosophic sets with application in multi-criteria decision making. Electronics, 12(1), 201. https://doi.org/10.3390/electronics12010201
Mustapha, N., Riza, F. F. A., Mansor, N. A., Mazlan, N. A. S., Alias, S., & Yasin, R. M. (2023). Utilizing the clustering techniques using distance-based similarity measures of SVNS in medical diagnosis. Applied Mathematics and Computational Intelligence, 12(4), 52–65. https://doi.org/10.58915/amci.v12i4.251
Dasan, M. A., Bementa, E., Aslam, M., & Flower, V. F. L. (2024). Multi-attribute decision-making problem in career determination using single-valued neutrosophic distance measure. Complex & Intelligent Systems, 10, 5411–5425. https://doi.org/10.1007/s40747-024-01433-z
Radha, N., & Samuel, A. E. (2024). Enhancing medical diagnosis with single valued neutrosophic sets and tan-log distance. Soft Computing Fusion with Applications, 1(1), 49–57. https://doi.org/10.22105/scfa.v1i1.29
Mishra, A. R., Pamucar, D., Rani, P., & Hezam, I. M. (2025). Single-valued neutrosophic distance measure-based MEREC-RANCOM-WISP for solving sustainable energy storage technology problem. Cognitive Computation, 17(2), 87. https://doi.org/10.1007/s12559-025-10437-x
Xu, D., & Zhao, Y. (2025). A new distance measure for single-valued neutrosophic set and an improved method based on TOPSIS and TODIM to multi-attribute decision-making. International Journal of Knowledge-Based and Intelligent Engineering Systems, 29(3), 322–335. https://doi.org/10.1177/13272314241313146
Thakur, S. S. (2025). Distance measures of (m, a, n)-fuzzy neutrosophic sets and their applications in decision making. Symmetry, 17(6), 939. https://doi.org/10.3390/sym17060939
Dasan, M. A., Lucase, L., AlAita, A., Aslam, M., & Bementa, E. (2026). A decision-making problem for smartphone selection using neutrosophic distance measures. Advances in Fuzzy Systems, 2026(1), Article 5585705. https://doi.org/10.1155/adfs/5585705
Irwin, R. S., French, C. L., Chang, A. B., & Altman, K. W. (2018). Classification of cough as a symptom in adults and management algorithms: CHEST guideline and expert panel report. Chest, 153(1), 196–209. https://doi.org/10.1016/j.chest.2017.10.016
Parshall, M. B., Schwartzstein, R. M., Adams, L., et al. (2012). An official American Thoracic Society statement: Update on the mechanisms, assessment, and management of dyspnea. American Journal of Respiratory and Critical Care Medicine, 185(4), 435–452. https://doi.org/10.1164/rccm.201111-2042ST
Marchello, C. S., Ebell, M. H., Dale, A. P., Harvill, E. T., Shen, Y., & Whalen, C. C. (2019). Signs and symptoms that rule out community-acquired pneumonia in outpatient adults: A systematic review and meta-analysis. The Journal of the American Board of Family Medicine, 32(2), 234–247. https://doi.org/10.3122/jabfm.2019.02.180219
All site content, except where otherwise noted, is licensed under the 