Combination of Hamming Distance and Entropy Measure of Picture Fuzzy Sets: Case Study of COVID-19 Medicine Selection

Nguyen, Xuan Thao; Nguyen, Quoc Hung; Le, Duy Dong; Pham, Hai Van

doi:10.1007/978-981-19-7513-4_52

Cited by 1 publication

(1 citation statement)

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“…However, Knowledge Graphs have difficulty representing knowledge and inferring output labels on medical symptom datasets with some characteristics such as amplitude and phase term, uncertain or incomplete input information. Some applications of Picture Fuzzy Set in disease diagnosis [15][16][17][18] or fuzzy techniques based on Fuzzy Inference System, such as Fuzzy Inference System [19][20][21][22][23][24][25][26][27], Complex Fuzzy Inference System [28][29][30], and Mamdani Complex Fuzzy Inference System [31,32] have overcome the limitations mentioned in Knowledge Graph models. These techniques can represent knowledge for datasets containing ambiguous and unclear information, but these models cannot find output labels for new samples that are not in the Fuzzy Rules Base.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Knowledge Graph Pairs-based Model in extreme cases for Hepatitis Sign Diagnosis

Khanh,

Long,

Hai

2023

Preprint

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Recently, machine learning models based on fuzzy inference systems have effectively solved problems in medical diagnosis and received much attention from researchers around the world. For example, Fuzzy Inference System (FIS), Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R), Mamdani Complex Fuzzy Inference System with Fuzzy Knowledge Graph (M-CFIS-FKG), and Fuzzy Knowledge Graph Pairs (FKG-Pairs) have been proposed. These machine-learning models have successfully solved most of the disease diagnosis problems based on complex symtom datasets with some characteristics such as amplitude and phase term, uncertain or incomplete input information. However, these models have revealed limitations when dealing with decision-making or disease diagnosis problems in extreme cases (where training datasets are too small or input datasets are large). To overcome this challenge, the FKG-Extreme model, based on FKG-Pairs combined with Q-learning techniques, has been proposed. In order to promote the practical application of the theoretical model FKG-Extreme, a model for Hepatitis Sign Diagnosis has been proposed in this paper. A simulation application of the proposed model has been built, and experimental results have been carried out based on the Liver dataset collected from the UCI machine learning repository, with results in terms of accuracy 13.3% higher than the FKG-Pairs model. Furthermore, some measures (Precision, Recall, and F1-Score) have also been used to evaluate performance and demonstrate that the proposed model has high reliability.

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