Abstract
In recent years, due to advancement in medical technologies and its devices, a large volume of medical data is generated continuously from different sources at every moment. Analyzing these large volumes of medical data and correctly diagnosing the diseases are challenging tasks. Generally, these medical data contain uncertain, imprecise, and incomplete information that affects the performance of the classification model. In this paper, Linguistic Neuro-Fuzzy (LNF) model is used for the classification of diseases. First, this model uses a linguistic fuzzification process that computes the membership values of each feature to overcome the uncertainty issues. Second, these membership values are passed to the ANN-based model to predict the disease. The objective of this research work lies in the applications of this LNF model that predicts the diseases. The effectiveness of this model is tested and validated using six benchmark medical datasets. The performance of the LNF model is compared with ANN and observed that the LNF model outperforms than ANN to handle the uncertainty problem.
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