Research Article

Enhancing Medical Diagnosis with Single Valued Neutrosophic Sets and Tan-Log Distance

Authors

Narmadhagnanam Radha * P.G. & Research Department of Mathematics, Government Arts College (Autonomous), Kumbakonam, Tamil Nadu, India‎.
A Edward Samuel P.G. & Research Department of Mathematics, Government Arts College (Autonomous), Kumbakonam, Tamil Nadu, India‎.

https://doi.org/10.22105/scfa.v1i1.29

Downloads

Abstract

Most diseases share a mix of common and unique traits, with distinct symptoms appearing at the onset. This paper proposes a method to establish relationships between illness classes and individuals with specific pathologies, aiding physicians in plausible diagnoses. We introduce the tan-log distance among single valued Neutrosophic Sets (NSs) and discuss its characteristics. This approach addresses uncertainties and shortcomings in current methods. The application of this method to medical diagnosis demonstrates its effectiveness in identifying illnesses. The previously mentioned approach's concept serves as a vital safeguard against uncertainties and shortcomings in the existing methods. To determine the sickness that a person is suffering, the application of medical diagnosis is discussed. The result of the diagnosis showed how effective the recommended course of action was.

Keywords

Neutrosophic set Single valued neutrosophic set Tan-log distance Medical diagnosis

References (63)

[1] Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

[2] Atanassov, K. T., & Stoeva, S. (1986). Intuitionistic fuzzy sets. Fuzzy sets and systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3

[3] Samuel, A. E., & Narmadhagnanam, R. (2018). Intuitionistic fuzzy multisets in medical diagnosis. International journal of fuzzy mathematical archive, 16(01), 13–19. https://doi.org/10.22457/ijfma.v16n1a3

[4] Mohamed Kozae, A., Shokry, M., & Omran, M. (2020). Intuitionistic fuzzy set and its application in Corona Covid-19. Applied and computational mathematics, 9(5), 146. https://doi.org/10.11648/j.acm.20200905.11

[5] Rajkalpana, M., KavyaSri, R., Abarna, S., & Nishantini, M. (2020). An application of decision-making problem using hexagonal and pentagonal fuzzy number matrix. International journal of advanced science and technology, 29(2), 2613–2617. http://sersc.org/journals/index.php/IJAST/article/view/3817

[6] Shinoj, T. K., & John, S. J. (2012). Intuitionistic fuzzy multisets and its application in medical diagnosis. World academy of science, engineering and technology, 6(1), 1418–1421. https://www.researchgate.net/publication/265089040

[7] Rajarajeswari, P., & Uma, N. (2013). Hausdroff similarity measures for intuitionistic fuzzy multi sets and its application in medical diagnosis. International journal of mathematical archive-4 (9), 4(9), 106–111.

[8] Rajarajeswari, P., & Uma, N. (2014). Normalized hamming similarity measure for intuitionistic fuzzy multi sets and its application in medical diagnosis. International journal of mathematical trends and technology, 5(3), 219–225. https://doi.org/10.14445/22315373/ijmtt-v5p525

[9] Samuel, A. E., & Narmadhagnanam, R. (2018). Intuitionistic fuzzy multisets in medical diagnosis. International journal of fuzzy mathematical archive, 16(1s), 13–19. http://dx.doi.org/10.22457/ijfma.v16n1a3

[10] Smarandache, F. (1999). A unifying field in logics: Neutrosophy: Neutrosophic probability, set and logic. Rehoboth: American research press. http://dx.doi.org/10.6084/M9.FIGSHARE.1014204

[11] Pawlak, Z. (1982). Rough sets. International journal of computer & information sciences, 11, 341–356. https://doi.org/10.1007/BF01001956

[12] Nanda, S., & Majumdar, S. (1992). Fuzzy rough sets. Fuzzy sets and systems, 45(2), 157–160. https://doi.org/10.1016/0165-0114(92)90114-J

[13] Broumi, S., Smarandache, F., & Dhar, M. (2014). Rough neutrosophic sets. Italian journal of pure and applied mathematics, 32, 493–502. http://dx.doi.org/10.5281/zenodo.30310

[14] Pramanik, S., & Mondal, K. (2015). Cosine similarity measure of rough neutrosophic sets and its application in medical diagnosis. Global journal of advanced research, 2(1), 212–220. http://dx.doi.org/10.5281/zenodo.23041

[15] Pramanik, S., & Mondal, K. (2015). Cotangent Similarity measure of rough neutrosophic sets and its application to medical diagnosis. Journal of new theory, 4, 90–102. http://dx.doi.org/10.5281/zenodo.23142

[16] Pramanik, S., Roy, R., Roy, T. K., & Smarandache, F. (2017). Multi criteria decision making using correlation coefficient under rough neutrosophic environment. Neutrosophic sets and systems, 17, 29–36. http://dx.doi.org/10.5281/zenodo.1012237

[17] Samuel, E., & Narmadhagnanam, R. (2018). Utilization of Rough neutrosophic sets in medical diagnosis. International journal of engineering science invention, 7(3), 1–15. https://fs.unm.edu/neut/UtilizationOfRoughNeutrosophicMedical.pdf

[18] Samuel, A. E., & Narmadhagnanam, R. (2018). Neoteric techniques for Rough Neutrosophic set and their utilization in Medical Diagnosis. International journal for research in engineering application & management, 4(7), 253–258.

[19] Samuel, A. E., & Narmadhagnanam, R. (2018). Rough neutrosophic sets in medical diagnosis. International journal of pure and applied mathematics, 120(8), 79–87. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.acadpubl.eu/hub/2018-120-8/1/9.pdf

[20] Samuel, A. E., & Narmadhagnanam, R. (2019). Pi-distance of rough Neutrosophic sets for medical diagnosis. Neutrosophic sets and systems, 28(1), 51–57. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://fs.unm.edu/NSS/PiDistanceOfRoughNeutrosophic.pdf

[21] Abdel-Basset, M., Mohamed, R., Zaied, A. E. N. H., & Smarandache, F. (2019). A hybrid plithogenic decision-making approach with quality function deployment for selecting supply chain sustainability metrics. Symmetry, 11(7), 903. https://doi.org/10.3390/sym11070903

[22] Abdel-Basset, M., Nabeeh, N. A., El-Ghareeb, H. A., & Aboelfetouh, A. (2020). Utilising neutrosophic theory to solve transition difficulties of IoT-based enterprises. Enterprise information systems, 14(9–10), 1304–1324. https://doi.org/10.1080/17517575.2019.1633690

[23] Nabeeh, N. A., Abdel-Basset, M., El-Ghareeb, H. A., & Aboelfetouh, A. (2019). Neutrosophic multi-criteria decision making approach for IoT-based enterprises. IEEE access, 7, 59559–59574. https://doi.org/10.1109/ACCESS.2019.2908919

[24] Abdel-Baset, M., Chang, V., & Gamal, A. (2019). Evaluation of the green supply chain management practices: A novel neutrosophic approach. Computers in industry, 108(1), 210–220. https://doi.org/10.1016/j.compind.2019.02.013

[25] Abdel-Basset, M., Saleh, M., Gamal, A., & Smarandache, F. (2019). An approach of TOPSIS technique for developing supplier selection with group decision making under Type-2 neutrosophic number. Applied soft computing journal, 77, 438–452. https://doi.org/10.1016/j.asoc.2019.01.035

[26] Abdel-Baset, M., Chang, V., Gamal, A., & Smarandache, F. (2019). An integrated neutrosophic ANP and VIKOR method for achieving sustainable supplier selection: A case study in importing field. Computers in industry, 106, 94–110. https://doi.org/10.1016/j.compind.2018.12.017

[27] Abdel-Basset, M., Manogaran, G., Gamal, A., & Smarandache, F. (2019). A group decision making framework based on neutrosophic TOPSIS approach for smart medical device selection. Journal of medical systems, 43(2), 1–13. https://doi.org/10.1007/s10916-019-1156-1

[28] Mohana, K., & Mohanasundari, M. (2019). On some similarity measures of single valued neutrosophic rough sets. Neutrosophic sets and systems, 24, 10–22. https://doi.org/10.5281/zenodo.2593905

[29] Bera, T., & Mahapatra, N. K. (2019). Generalised single valued neutrosophic number and its application to neutrosophic linear programming. Neutrosophic sets and systems, 25, 85–103. https://digitalrepository.unm.edu/cgi/viewcontent.cgi?article=1258&context=nss_journal

[30] Ulucay, V., Kilic, A., Yildiz, I., & Șahin, M. (2018). A new approach for multi-attribute decision-making problems in bipolar neutrosophic sets. Neutrosophic sets and systems, 23(1), 142–159. http://dx.doi.org/10.5281/zenodo.2154873

[31] Wang, H., Smarandache, F., Sunderraman, R., & Zhang, Y. Q. (2010). Single valued neutrosophic sets. Multispace and multistructure, 4, 410–413. https://www.researchgate.net/publication/262047656

[32] 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

[33] 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

[34] Broumi, S., Murugappan, M., Talea, M., Smarandache, F., Bakali, A., Singh, P. K., & Dey, A. (2019). Single valued (2N+ 1) sided polygonal neutrosophic numbers and single valued (2N) sided polygonal neutrosophic numbers. Neutrosophic sets and systems, 25, 54–65. https://fs.unm.edu/nss8/index.php/111/article/view/153

[35] Li, C., Ye, J., Cui, W., & Du, S. (2019). Slope stability assessment method using the arctangent and tangent similarity measure of neutrosophic numbers. Neutrosophic sets and systems, 27(1), 98–103. http://dx.doi.org/10.5281/zenodo.3275419

[36] Samuel, A. E., & Narmadhagnanam, R. (2018). Cosine logarithmic distance of singlevalued neutrosophic sets in medical diagnosis. International journal of engineering, science and mathematics, 7(6), 14–19. https://ijesm.co.in/uploads/68/5583_pdf.pdf

[37] Samuel, A. E., & Narmadhagnanam, R. (2018). Tangent inverse similarity measure of singled valued neutrosophic sets in medical diagnosis. International journal of creative research thoughts, 6(2), 77–79. https://www.ijcrt.org/papers/IJCRT1892673.pdf

[38] Garg, H. (2020). Algorithms for single-valued neutrosophic decision making based on TOPSIS and clustering methods with new distance measure. Infinite Study. https://B2n.ir/es8102

[39] 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 and intelligent systems, 7(2), 703–723. https://doi.org/10.1007/s40747-020-00220-w

[40] Narmadhagnanam, R., & Samuel, A. E. (2022). Sine exponential measure of single valued neutrosophic sets in medical diagnosis. Neutrosophic sets and systems, 51, 303–310. https://doi.org/10.5281/zenodo.7135305

[41] Ye, S., & Ye, J. (2014). Dice similarity measure between single valued neutrosophic multisets and its application in medical diagnosis. Neutrosophic sets and systems, 6(1), 48–53. https://www.researchgate.net/publication/308789878

[42] Samuel, A. E., & Narmadhagnanam, R. (2018). Cosine exponential distance of single valued neutrosophic multi sets in medical diagnosis. International journal of engineering, research and application, 8(6), 26–29. https://fs.unm.edu/neut/CosineExponentialDistance.pdf

[43] Smarandache, F. (2013). N-valued refined neutrosophic logic and its applications to physics. Progress in physics, 4, 143–146. http://dx.doi.org/10.5281/zenodo.30245

[44] Broumi, S., & Smarandache, F. (2014). Neutrosophic refined similarity measure based on cosine function. Neutrosophic sets and systems, 6, 4–10. http://dx.doi.org/10.5281/zenodo.30230

[45] Ye, J. (2014). Vector similarity measures of simplified neutrosophic sets and their application in multicriteria decision making. International journal of fuzzy systems, 16(2), 204–211. https://www.researchgate.net/publication/281546974

[46] Samuel, A. E., & Narmadhagnanam, R. (2017). Neutrosophic refined sets in medical diagnosis. International journal of fuzzy mathematical archive, 14(1), 117–123. https://doi.org/10.22457/ijfma.v14n1a14

[47] Samuel, A. E., & Narmadhagnanam, R. (2018). Sine logarithmic distance of neutrosophic refined sets in medical diagnoses. Journal of global research in mathematical archive, 5(6), 1–5. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://vixra.org/pdf/1808.0459v1.pdf

[48] Samuel, A. E., & Narmadhagnanam, R. (2018). Contemporary techniques for neutrosophic refined set and their exploitation in medical diagnosis. Journal of emerging technologies and innovative research, 4(6), 25–29.

[49] Zhao, M., & Ye, J. (2022). MCGDM approach using the weighted hyperbolic sine similarity measure of neutrosophic (Indeterminate fuzzy) multivalued sets for the teaching quality assessment of teachers. Neutrosophic sets and systems, 48(1), 1-8. http://dx.doi.org/10.5281/zenodo.6041272

[50] Kalla, H., Kumar, B. S., & Smarandache, F. (2023). Medical diagnosis via refined neutrosophic fuzzy logic: detection of illness using neutrosophic sets. Journal of advanced zoology, 44(1), 15–24. https://doi.org/10.17762/jaz.v44is-1.197

[51] Wang, H., Smarandache, F., Zhang, Y. Q., & Sunderraman, R. (2005). Interval neutrosophic sets and logic: Theory and applications in computing. Infinite Study. http://dx.doi.org/10.5281/zenodo.8818

[52] Uluçay, V. (2021). Some concepts on interval-valued refined neutrosophic sets and their applications. Journal of ambient intelligence and humanized computing, 12(7), 7857–7872. https://doi.org/10.1007/s12652-020-02512-y

[53] Pouresmaeil, H., Khorram, E., & Shivanian, E. (2022). A parametric scoring function and the associated method for interval neutrosophic multi-criteria decision-making. Evolving systems, 13(2), 347–359. https://doi.org/10.1007/s12530-021-09394-1

[54] Liu, Q., Wang, X., Kong, M., & Qin, K. (2023). Similarity measure for interval neutrosophic sets and its decision application in resource offloading of edge computing. Electronics (Switzerland), 12(8), 1931. https://doi.org/10.3390/electronics12081931

[55] Ye, J., Du, S., & Yong, R. (2021). Correlation coefficients of credibility interval-valued neutrosophic sets and their group decision-making method in single- and interval-valued hybrid neutrosophic multi-valued environment. Complex and intelligent systems, 7(6), 3225–3239. https://doi.org/10.1007/s40747-021-00500-z

[56] Hema, R., Sudharani, R., & Kavitha, M. (2023). A novel approach on plithogenic interval valued neutrosophic hyper-soft sets and its application in decision making. Indian journal of science and technology, 16(32), 2494–2502. http://dx.doi.org/10.17485/IJST/v16i32.1302

[57] Saqlain, M., Kumam, P., & Kumam, W. (2024). Neutrosophic linguistic valued hypersoft set with application: medical diagnosis and treatment. Neutrosophic sets and systems, 63, 130–152. https://doi.org/10.5281/zenodo.10531779

[58] Broumi, S., Deli, I., & Smarandache, F. (2015). N-valued interval neutrosophic sets and their application in medical diagnosis. Critical review, 10, 45–69. https://doi.org/10.6084/M9.FIGSHARE.1502596

[59] Samuel, A. E., & Narmadhagn, R. (2017). Innovative approaches for n-valued interval neutrosophic sets and their execution in medical diagnosis. Journal of applied sciences, 17(9), 429–440. https://doi.org/10.3923/jas.2017.429.440

[60] Samuel, A. E., & Narmadhagnanam, R. (2018). N-valued interval neutrosophic set in medical diagnosis. International journal of pure and applied mathematics, 120(8), 59–67. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://acadpubl.eu/hub/2018-120-8/2/7.pdf

[61] Samuel, A. E., & gnanam, R. N. (2018). Execution of n-valued interval neutrosophic sets in medical diagnosis. International journal of mathematics trends and technology, 58(1), 66–70. https://doi.org/10.14445/22315373/ijmtt-v58p509

[62] Narmadhagnanam, R., & Samuel, A. E. (2024). Application of secant span in medical diagnosis. Neutrosophic systems with applications, 18, 40–45. https://doi.org/10.61356/j.nswa.2024.18254

[63] Broumi, S., & Smarandache, F. (2015). Extended hausdorff distance and similarity measures for neutrosophic refined sets and their application in medical diagnosis. Journal of new theory, 7, 64–78. https://dergipark.org.tr/en/pub/jnt/issue/34499/381190

Article Metrics

Published 2024-03-25
Issue Vol. 1 No. 1 (2024): Soft Computing Fusion with Applications
Section Articles
Document Type Research Article
Views 1156
Downloads 445

Categories

Computational Intelligence Techniques

How to Cite

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), 50-58. https://doi.org/10.22105/scfa.v1i1.29

Download Citation

Similar Articles

Narmadhagnanam Radha, A. Edward Samuel, Exploring Novel Application of Single-Valued Neutrosophic Sets , Soft Computing Fusion with Applications : Vol. 3 No. 1 (2026): Soft Computing Fusion with Applications
Daniel Dasig Jr, A Fuzzy Set-Based Context-Aware Decision Framework for Histopathological Image Classification in Tumor Microarrays , Soft Computing Fusion with Applications : Vol. 2 No. 2 (2025): Soft Computing Fusion with Applications
Mojtaba Nasehi, Advanced Applications of Fuzzy Systems and Computational Intelligence in Decision-Support Systems , Soft Computing Fusion with Applications : Vol. 1 No. 4 (2024): Soft Computing Fusion with Applications
Habib Alijani, Samira Torabi, A Comparative Study of Machine Learning Techniques for Earthquake Magnitude Prediction , Soft Computing Fusion with Applications : Vol. 2 No. 4 (2025): Soft Computing Fusion with Applications
Marzieh Lamtar-Gholipoor, Haruna Mavakumba Kefas, Khris Benjamin Swain, Metaheuristic Optimization in Biological Sciences: A Narrative Critical Review of Bioinformatics, Ecology, and Systems Biology , Soft Computing Fusion with Applications : Vol. 3 No. 3 (2026): Soft Computing Fusion with Applications
Javad Pourqasem, Bhumika Diwan, Zeinab Parandavar, Marine Predators Algorithm for Compact Feature Selection on the Wisconsin Diagnostic Breast Cancer Benchmark , Soft Computing Fusion with Applications : Vol. 3 No. 2 (2026): Soft Computing Fusion with Applications (SCFA)
Mahmoud Alimoradi, Awaz Ahmed Shaban, Isametova Madina Esdauletova, Label-Efficient Active Learning on Clinical-Style Tabular Data: Uncertainty, Margin, and Committee Strategies Across Imbalance and Noise Regimes , Soft Computing Fusion with Applications : Vol. 3 No. 2 (2026): Soft Computing Fusion with Applications (SCFA)
Seyyed Ahmad Edalatpanah, Ismail A. Mageed, Lorenzo Cevallos-Torres, Image Covariates and Label Noise Robustness in Dermatoscopic Classification , Soft Computing Fusion with Applications : Vol. 3 No. 3 (2026): Soft Computing Fusion with Applications
Imoh Ime Ekanem, Aniekan Essienubong Ikpe, Jephtar Uviefovwe Ohwoekevwo, A Study on IoT-Enabled Smart Vehicles for Road Navigation and Ride Comfortability in Contemporary Vehicle Applications , Soft Computing Fusion with Applications : Vol. 1 No. 2 (2024): Soft Computing Fusion with Applications
Aurgho Banerjee, Securing the Future: AI-Driven Data Transmission in IoT-Powered Smart Cities , Soft Computing Fusion with Applications : Vol. 2 No. 1 (2025): Soft Computing Fusion with Applications

1-10 of 63 Next

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

Narmadhagnanam Radha, A. Edward Samuel, Exploring Novel Application of Single-Valued Neutrosophic Sets , Soft Computing Fusion with Applications : Vol. 3 No. 1 (2026): Soft Computing Fusion with Applications