Affiliations: [a] Department of Computer Science & Engineering, Bapuji Institute of Engineering & Technology, Davangere, Karnataka, India
| [b] Research Institute for Natural Sciences, Hanyang University, Seoul, Korea
| [c] Department of Mathematics, Bapuji Institute of Engineering & Technology, Davangere, Karnataka, India
| [d] Department of Mathematics, Riphah International University Lahore, Pakistan
Correspondence:
[*]
Corresponding author. Choonkil Park, Research Institute for Natural Sciences, Hanyang University, Seoul 04763, Korea. E-mail: [email protected].
Abstract: 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.
Keywords: Bat-based random forest, fuzzy value, optimization
