Sep 2026· Knowledge and Information Systems· Vol 68· 0 citations· 43 references
TL;DR
The proposed Fuzzy Ebola Search Optimized Dynamic K-means Clustering Algorithm (FESO-DKMCA) addresses the problem of medical knowledge being outdated by having fuzzy membership functions that constantly reassign data points to clusters based on degree of belonging rather than strict boundaries, allowing the model to dynamically adapt to changing medical datasets and ensure that decisions remain grounded in current knowledge.
The concept of Rough Set Theory along with the Rule Generation Techniques in Medical Diagnosis in general and particularly for the identification of heart disease through the Core and Reduct of the concerned attributes of an information system are presented.
Pragati Jain, K. Agrawal, Deepensha Vaishnav· 3 citations
We are living in the era known as information era where numerous sectors especially health sectors are put to handle tremendous amount of information. To reduce this burden, an efficient data mining technology has been employed which is a successful evolving technique that has a big future for helping businesses and co...
T. Thamaraiselvan, K. Saravanan, S. Nithyanandam· Journal of Intelligent Decis...· 0 citations
Machine learning (ML) models, including rule-based architectures dominant in the technology of fuzzy sets, are constructed with the aid of intensive usage of data. Quite often, knowledge about the system/problem is available and offers some tangible benefits to the construction of the models. The relevance of knowledge...
Witold Pedrycz· IEEE Transactions on Cyberne...· 0 citations
The fuzzy-based use of Artificial Intelligence (AI) in healthcare could change operations research. There is a possibility that the development of AI will be the spark that triggers this revolution. Artificial intelligence can handle healthcare data's ambiguities and complexity, improving Decision-Making (DM), Resource...
S. Balakrishnan, Sridhara Bejugama, A. Suresh et al.· Yugoslav journal of operatio...· 0 citations
Fuzzy clustering algorithms are rapidly applied in medical data analysis because they can capture uncertainty and overlapping patterns more effectively than traditional hard clustering methods. This paper presents a comparative study of five widely studied approaches such as Conventional Fuzzy C-Means, Kernelized Fuzz...
Neha Kumari Mewalal Bhatiya, Bhoomiben Dilipbhai Patel, R. Gor et al.· Boletim da Sociedade Paranae...· 0 citations
Artificial intelligence (AI) and machine learning (ML) have achieved remarkable performance across diverse domains, but many models still struggle with uncertainty, vagueness, and interpretability. Fuzzy logic, originating from Zadeh’s seminal work on fuzzy sets, provides a mathematical framework to model linguistic co...
Adil Ahmad, Muhammad Ahmad, M. Jafar· IETI Transactions on Data An...· 0 citations
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