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International Journal of Neutrosophic Science
Volume 23 , Issue 3, PP: 373-245 , 2024 | Cite this article as | XML | Html |PDF

Title

Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection

  Sakshi Taaresh Khanna 1 * ,   Sunil Kumar Khatri 2 ,   Neeraj Kumar Sharma 3

1  Amity Institute of Information Technology, Amity University, Uttar Pradesh, Noida-201313, India
    (sakshitaareshkhanna@gmail.com)

2  Amity University, Uttar Pradesh, Noida-201313, India
    (skkhatri@amity.edu)

3  Department of Computer Science, Ram Lal Anand College, Benito Juarez Marg, New Delhi-110021, India
    (neerajksharma100@gmail.com)

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Doi   :   https://doi.org/10.54216/IJNS.230328

Received: August 17, 2023 Revised: December 18, 2023 Accepted: February 26, 2024

Abstract :

Among the current generation researcher, artificial intelligence has played vital role in various fields, including healthcare. One of the key areas where it has shown enormous potential is in cancer detection and treatment. AI and methods of machine learning algorithms have been applied to analyze large datasets, such as genomics, transcriptomic, and imaging data, to identify patterns and relationships that can help in cancer diagnosis and therapy. However, due to the inherent complexity and heterogeneity of tumors in individual patients, building a diagnostic and therapeutic platform that can accurately analyze outputs becomes a challenging task. To address this challenge, researchers have proposed the use of explainable AI frameworks in cancer detection. Explainable AI frameworks aim to provide transparency and comprehensibility to the decision-making process of AI algorithms, ensuring that the predictions or classifications generated by these algorithms can be understood and trusted by healthcare professionals. One popular explainable AI method is SHAP (SHapley Additive explanations). SHAP is a well-known XAI method that provides intuitive and interpretable feature importance [13] for individual predictions. Another explainable AI method is LIME (Local Interpretable Model-agnostic Explanations), which generates posthoc explanations and is suitable for quick and satisfactory explanations. These existing explainable AI methods, however, have limitations in their applicability to cancer detection. Therefore, in this research article, we propose the use of two novel frameworks: Neutrosophic Meta SHAP and Neutrosophic Meta Lime. Neutrosophic Meta SHAP and Neutrosophic Meta Lime are efficient frameworks specifically designed for the analysis and interpretation of AI models in oral cancer detection.

Keywords :

Machine Learning in Disease prediction; ANN; SVM; GBM; Neutrosophic Meta SHAP; Neutrosophic Meta LIME; Oral Cancer

References :

[1]      Abati, S., Bramati, C., Bondi, S., Lissoni, A. and Trimarchi, M., 2020. Oral cancer and precancer: a narrative review on the relevance of early diagnosis. International journal of environmental research and public health, 17(24), p.9160.

[2]      Ahmed A. El-Douh, SongFeng Lu, Ahmed Abdelhafeez, Ahmed M. Ali, & Alber S. Aziz. (2023). Heart Disease Prediction under Machine Learning and Association Rules under Neutrosophic Environment. Neutrosophic Systems with Applications, 10, 35–52. https://doi.org/10.61356/j.nswa.2023.75

[3]      Ali, M., van Minh, N., Son, L. H., Trai, N., Xuan, T., & Hanoi, V. (n.d.). A Neutrosophic Recommender System for Medical Diagnosis Based on Algebraic Neutrosophic Measures.

[4]      Atanassov, K. T. (1986) Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, 87–96.

[5]      Bandera, N. H., Arizaga, J. M. M., & Reyes, E. R. (2023). Assessment and prediction of Chronic Kidney using an improved neutrosophic artificial intelligence model. International Journal of Neutrosophic Science, 21(1), 174–183. https://doi.org/10.54216/IJNS.210116

[6]      Bandera, N. H., Arizaga, J. M. M., & Reyes, E. R. (2023). Neutrosophic Multi-criteria Decision-making Methodology for Evaluation chronic obstructive pulmonary disease. International Journal of Neutrosophic Science, 21(1), 184–191. https://doi.org/10.54216/IJNS.210117

[7]      Bouvard, V., Nethan, S.T., Singh, D., Warnakulasuriya, S., Mehrotra, R., Chaturvedi, A.K., Chen, T.H.H., Ayo-Yusuf, O.A., Gupta, P.C., Kerr, A.R. and Tilakaratne, W.M., 2022. IARC perspective on oral cancer prevention. New England Journal of Medicine, 387(21), pp.1999-2005.

[8]      Chattopadhyay, I., Verma, M., & Panda, M. (2019). Role of Oral Microbiome Signatures in Diagnosis and Prognosis of Oral Cancer. Technology in Cancer Research & Treatment, 18, pp.1-9. https://doi.org/10.1177/1533033819867354

[9]      Del Sol, A. B., Nivela, E. S., Solis, E. M., & Elawady, Y. H. (2023). Neutrosophic Hybrid Machine Learning Algorithm for Diabetes Disease Prediction. International Journal of Neutrosophic Science, 21(2), 75–83. https://doi.org/10.54216/IJNS.210207.

[10]   Gupta, S., Gupta, M. K., Shabaz, M., & Sharma, A. (2022). Deep learning techniques for cancer classification using microarray gene expression data. In Frontiers in Physiology (Vol. 13). Frontiers Media S.A. https://doi.org/10.3389/fphys.2022.952709.

[11]   Inchingolo, F., Santacroce, L., Ballini, A., Topi, S., Dipalma, G., Haxhirexha, K., Bottalico, L. and Charitos, I.A., 2020. Oral cancer: A historical review. International journal of environmental research and public health, 17(9), p.3168.

[12]   Irani, S., 2020. New insights into oral cancer—Risk factors and prevention: A review of literature. International Journal of Preventive Medicine, 11.

[13]   Kungumaraj.E, Durgadevi.S, Tharani.P, Heptagonal Neutrosophic Topology, Neutrosophic Sets and Systems, Vol.60, (2023) pp-335-356.

[14]   Broumi, S., Nagarajan, D., and Bakali. A., "The shortest path problem in interval valued trapezoidal and triangular neutrosophic environment”, Complex Intell. Syst. Vol.5, pp. 391–402.2019.https://doi.org/10.1007/s40747-019-0092-5

[15]   Li, X., Xiong, H., Li, X., Wu, X., Zhang, X., Liu, J., Bian, J. and Dou, D., 2022. Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond. Knowledge and Information Systems, 64(12), pp.3197-3234.

[16]   Lin, H., Chen, H., Weng, L., Shao, J. and Lin, J., 2021. Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis. Journal of Biomedical Optics, 26(8), pp.086007-086007.

[17]   Broumi, S., Raut, P. K., & Behera, S. P. (2023). Solving shortest path problems using an ant colony algorithm with triangular neutrosophic arc weights. International Journal of Neutrosophic Science, 20(4), 128-28.

[18]   Mohammadi, M., Rashid, T.A., Karim, S.H.T., Aldalwie, A.H.M., Tho, Q.T., Bidaki, M., Rahmani, A.M. and Hosseinzadeh, M., 2021. A comprehensive survey and taxonomy of the SVM-based intrusion detection systems. Journal of Network and Computer Applications, 178, p.102983

[19]   Nguyen, H.T.T., Cao, H.Q., Nguyen, K.V.T. and Pham, N.D.K., 2021. Evaluation of explainable artificial intelligence: Shap, lime, and cam. In Proceedings of the FPT AI Conference (pp. 1-6).

[20]   Padilla, P. A., Rubio, E. B., Valdiviezo, W. V., & Hassan, M. K. (2023). Heart Disease Prediction using Neutrosophic C-Means Clustering Algorithm. International Journal of Neutrosophic Science, 21(2), 68–74. https://doi.org/10.54216/IJNS.210206

[21]   Rodríguez, J. V., Martínez, J. R. Salazar, N. H., (2023). Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study. International Journal of Neutrosophic Science, 21(2), 118–128. https://doi.org/10.54216/IJNS.210211.

[22]   Rodríguez, J. V., Martínez, J. R., & Jumbo Salazar, F. F. (2023). Colorectal Cancer Prediction Using Machine Learning and Neutrosophic MCDM Methodology: A Case Study. International Journal of Neutrosophic Science, 21(2), 118–128. https://doi.org/10.54216/IJNS.210211

[23]   Rufo, D.D., Debelee, T.G., Ibenthal, A. and Negera, W.G., 2021. Diagnosis of diabetes mellitus using gradient boosting machine (LightGBM). Diagnostics, 11(9), p.1714.

[24]   Rymarczyk, T., Kozłowski, E., Kłosowski, G. and Niderla, K., 2019. Logistic regression for machine learning in process tomography. Sensors, 19(15), p.3400.

[25]   SalamaA.A., Belal Amin, El-Henawy. I.M., Khaled Mahfouz, Mona G. Gafar, "Intelligent Neutrosophic Diagnostic System for Cardiotocography Data", Computational Intelligence and Neuroscience, vol. 2021, Article ID 6656770, 12 pages, 2021. https://doi.org/10.1155/2021/6656770

[26]   Samarandache.F, Neutrosophic Set – A generalization of the Intuitionistic fuzzy set, International Journal of Pure and Applied Mathematics, Vol.24, No.3, 2005, pp.287-297.

[27]   Sarode, G., Maniyar, N., Sarode, S.C., Jafer, M., Patil, S. and Awan, K.H., 2020. Epidemiologic aspects of oral cancer. Disease-a-Month, 66(12), p.100988.

[28]   Singh, S. K., Abolghasemi, V., & Anisi, M. H. (2022). Skin Cancer Diagnosis Based on Neutrosophic Features with a Deep Neural Network. Sensors, 22(16). https://doi.org/10.3390/s22166261

[29]   Tseng, Y.J., Wang, H.Y., Lin, T.W., Lu, J.J., Hsieh, C.H. and Liao, C.T., 2020. Development of a machine learning model for survival risk stratification of patients with advanced oral cancer. JAMA network open, 3(8), pp.2011768-2011768.

[30]   Warnakulasuriya, S., Kujan, O., Aguirre‐Urizar, J.M., Bagan, J.V., González‐Moles, M.Á., Kerr, A.R., Lodi, G., Mello, F.W., Monteiro, L., Ogden, G.R. and Sloan, P., 2021. Oral potentially malignant disorders: A consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer. Oral diseases, 27(8), pp.1862-1880.

[31]   Welikala, R.A., Remagnino, P., Lim, J.H., Chan, C.S., Rajendran, S., Kallarakkal, T.G., Zain, R.B., Jayasinghe, R.D., Rimal, J., Kerr, A.R. and Amtha, R., 2020. Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access, 8, pp.132677-132693.

[32]   Zadeh, L.A. (1965) Fuzzy sets, Information and Control, 338–353.

Cite this Article as :
Style #
MLA Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. "Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection." International Journal of Neutrosophic Science, Vol. 23, No. 3, 2024 ,PP. 373-245 (Doi   :  https://doi.org/10.54216/IJNS.230328)
APA Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. (2024). Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection. Journal of International Journal of Neutrosophic Science, 23 ( 3 ), 373-245 (Doi   :  https://doi.org/10.54216/IJNS.230328)
Chicago Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. "Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection." Journal of International Journal of Neutrosophic Science, 23 no. 3 (2024): 373-245 (Doi   :  https://doi.org/10.54216/IJNS.230328)
Harvard Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. (2024). Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection. Journal of International Journal of Neutrosophic Science, 23 ( 3 ), 373-245 (Doi   :  https://doi.org/10.54216/IJNS.230328)
Vancouver Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma. Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection. Journal of International Journal of Neutrosophic Science, (2024); 23 ( 3 ): 373-245 (Doi   :  https://doi.org/10.54216/IJNS.230328)
IEEE Sakshi Taaresh Khanna, Sunil Kumar Khatri, Neeraj Kumar Sharma, Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection, Journal of International Journal of Neutrosophic Science, Vol. 23 , No. 3 , (2024) : 373-245 (Doi   :  https://doi.org/10.54216/IJNS.230328)
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