Type 2 Fuzzy Differential Evolution Based Semantic Ontology Approach for the Detection and Diagnosis of Diabetes


  • V. Manikandabalaji, R. Sivakumar


Diabetes detection, Type 2 fuzzy logic, differential evolution, semantic ontology


Diabetic detection and diagnosis is crucial in the medical field for efficient treatment and management. Conventional approaches frequently rely on time-consuming and error-prone manual analysis of medical records and symptoms. In order to overcome these obstacles, this paper proposes a Type 2 Fuzzy Differential Evolution based Semantic Ontology (T2FDESO) method for diabetes detection and diagnosis. The T2FDESO method improves diagnosis precision and speed by combining the strengths of fuzzy logic, differential evolution, and semantic ontology. The method utilizes Type 2 fuzzy logic to account for the gaps and inaccuracies in medical data, thereby facilitating more sound decision-making. Optimization of the diabetes detection model parameters using the differential evolution algorithm is used to boost its effectiveness. Semantic ontology is used in the T2FDESO method to create a standardized way to represent medical knowledge and the connections between various medical concepts. The system is able to effectively reason and infer diabetes-related information from the provided symptoms and patient data. The diagnostic process is improved thanks to the semantic ontology ability to facilitate the incorporation of domain-specific knowledge. In addition to the improved precision and speed of diabetes diagnosis, the T2FDESO method offers several other advantages. The utilization of semantic ontology allows for easy integration of expert knowledge from different fields, ensuring that the diagnostic system remains up-to-date with the latest advancements and insights in diabetes research and clinical practice. Furthermore, the T2FDESO approach enables the efficient integration of disparate data sources, including clinical records and laboratory test results, leading to a more comprehensive analysis of patient information. By capturing and hierarchically organizing domain-specific information, the system can make more informed decisions, leading to better patient outcomes. The experimental results with a real-world dataset demonstrate the superiority of the T2FDESO method over existing techniques, establishing its potential to revolutionize diabetes detection and diagnosis in the medical field. Its ability to enhance decision-making and timely treatment management can significantly impact healthcare providers' ability to provide personalized and effective care to individuals with diabetes.


Download data is not yet available.


American Diabetes Association. (2020). 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2020. Diabetes care, 43(Supplement_1), S14-S31.

Rawshani, A., Rawshani, A., Franzén, S., Eliasson, B., Svensson, A.-M., Miftaraj, M., ... & Gudbjörnsdottir, S. (2018). Mortality and cardiovascular disease in type 1 and type 2 diabetes. New England Journal of Medicine, 376(15), 1407-1418.

Ling, Y., Chen, Y., & Wang, H. (2017). Fuzzy rule-based clinical decision support system for diabetes management. Health Information Science and Systems, 5(1), 1-11.

Lim, G. Y., & Ng, Y. Y. (2014). Medical diagnosis of diabetes using fuzzy expert system. 2014 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 81-84.

Das, S., Abraham, A., & Konar, A. (2008). Differential evolution using fuzzy logic. IEEE Transactions on Evolutionary Computation, 12(2), 153-164.

Otero, F. E., & Pinto, A. S. (2014). Semantic ontology for diagnosis of tuberculosis. Journal of Biomedical Informatics, 48, 71-86.

Li, D., & Pedrycz, W. (2018). Type-2 fuzzy sets and systems: An overview. IEEE Computational Intelligence Magazine, 13(3), 54-68.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.

Gong, D., Wu, J., & Xu, L. (2017). Hybrid differential evolution algorithm for constrained optimization problems. Neurocomputing, 240, 91-100.

Vadas, D., Currie, M., & Lin, C. (2016). Ontology-based clinical decision support system: Toward the semantic electronic health record. Journal of Biomedical Informatics, 59, 42-54.

Majumdar, S., & Verma, A. K. (2015). Fuzzy ontology and ontology mapping based intelligent system for decision support. Journal of Ambient Intelligence and Humanized Computing, 6(2), 245-263.

Zhang, L., Zhang, Q., & Wang, G. G. (2009). Differential evolution for permutation flow-shop scheduling problems with fuzzy processing time. Information Sciences, 179(20), 3506-3522.

Chen, Y., Ling, Y., & Wang, H. (2019). A Fuzzy Ontology-Based Diabetes Decision Support System. International Journal of Environmental Research and Public Health, 16(9), 1533.

Singh, A. K., & Gupta, V. (2020). Type-2 Fuzzy Ontology for Diabetes Diagnosis. In Proceedings of the International Conference on Artificial Intelligence and Sustainable Technologies (pp. 683-692). Springer.

Shaik, A. R., Patra, M. R., & Rao, G. P. (2020). Type-2 fuzzy ontology-based system for diabetes diagnosis. In Advances in Machine Learning and Data Science (pp. 407-416). Springer.

Şahin, C., & Küçük, D. (2021). A hybrid decision support system based on fuzzy ontology and support vector machines for diabetes diagnosis. Health Information Science and Systems, 9(1), 1-12.

Arunmozhi, A., & Thirunavukarasu, P. (2020). Intelligent fuzzy ontology system for diabetes diagnosis. International Journal of Intelligent Systems and Applications, 12(10), 1-9.

Abiodun, A., Olugbara, O. O., & Ng, W. K. (2016). Differential evolution algorithms for classification of diabetes diagnosis. Journal of Medical Systems, 40(9), 1-13.

Vafaei, M. S., & Fakhrzadeh, H. (2017). Differential evolution optimized support vector machine for diabetes classification. Computer Methods and Programs in Biomedicine, 152, 113-119.

Hossain, M. A., Akhtar, M. F., & Serpedin, E. (2020). Differential evolution-based medical decision support system for diabetes diagnosis using feature selection. IEEE Access, 8, 87610-87620.

Chen, Y., Ling, Y., & Wang, H. (2018). A hybrid fuzzy logic and differential evolution approach for diabetes prediction. IEEE Access, 6, 53137-53145.

Shafique, F., Butt, S. A., Javaid, N., & Ahmad, A. (2020). A hybrid type-2 fuzzy ontology system for diabetes diagnosis. International Journal of Machine Learning and Cybernetics, 11(4), 855-867.

Guo, Y., Liu, Z., & Li, Y. (2021). A fuzzy ontology-based decision support system for diabetes diagnosis. Journal of Ambient Intelligence and Humanized Computing, 12(9), 11061-11072.

Qu, G., & Zhang, Y. (2021). A hybrid differential evolution algorithm for diabetes diagnosis. Complexity, 2021, 1-13.

Diabetes Mellitus Treatment Ontology - Summary | NCBO BioPortal (bioontology.org), https://bioportal.bioontology.org/ontologies/DMTO/?p=summary




How to Cite

V. Manikandabalaji. (2024). Type 2 Fuzzy Differential Evolution Based Semantic Ontology Approach for the Detection and Diagnosis of Diabetes. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2358–2371. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5840



Research Article