Type 2 Fuzzy Differential Evolution Based Semantic Ontology Approach for the Detection and Diagnosis of Diabetes
Keywords:
Diabetes detection, Type 2 fuzzy logic, differential evolution, semantic ontologyAbstract
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.
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Diabetes Mellitus Treatment Ontology - Summary | NCBO BioPortal (bioontology.org), https://bioportal.bioontology.org/ontologies/DMTO/?p=summary
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