Disease categorization with clinical data using optimized bat algorithm and fuzzy value

Diagnosis of human disease is a more difficult and complex process since it requires the consideration of various factors and symptoms to make a decision. Generally, the classification of diseases with fuzzy values is the most interesting topic because of accurate results. In this paper, we design a...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2023-01, Vol.44 (3), p.5467-5479
Main Authors: Naresh Patel, K.M., Ashoka, K., Park, Choonkil, Shanmukha, M.C., Azeem, Muhammad
Format: Article
Language:English
Subjects:
Algorithms
Classification
Decision trees
Disease
Signs and symptoms
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Summary:Diagnosis of human disease is a more difficult and complex process since it requires the consideration of various factors and symptoms to make a decision. Generally, the classification of diseases with fuzzy values is the most interesting topic because of accurate results. In this paper, we design a Bat-based Random Forest (BbRF) framework to enhance the performance of categorizing diseases with fuzzy values which also protect the privacy of the developed scheme. It involves pre-processing, attributes selection, fuzzy value generation, and classification. Additionally, the developed framework is implemented in Python tool and patient disease datasets are used for implementation. Moreover, pre-processing remove the error and noise, attributes are selected based on the duration of diseases. Finally, classify the patient disease based on the generated fuzzy value. To prove the efficiency of the developed framework, attained results are compared with other existing techniques in terms of accuracy, sensitivity, specificity, F-measure, and precision.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-222749