BLDA-CSWDT autoimmune thyroid disease risks predictive model using machine learning and deep feature extraction techniques Online publication date: Tue, 03-Oct-2023
by Nagavali Saka; S. Murali Krishna
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 43, No. 2, 2023
Abstract: Nowadays, different thyroid disorders are observed which are affecting the human population worldwide. Hence, to provide suitable treatment and be cost-consuming for the patients, an earlier diagnosis is required. To improve prediction, this paper proposed Bayes-linear discriminant analysis (B-LDA) and cuckoo search based weighted decision tree (CSWDT) models to predict the autoimmune thyroid risk assessment from the obtained dataset. Initially, after pre-processing, the features are extracted using the deep MLP model, and the significant features are fused by using the B-LDA model which overcomes the dimensionality reduction issue. Further, the classification is performed by using the optimised cuckoo search with a weighted decision tree model. In addition, K-fold cross-validation is performed and attains a better accuracy value of 99.5% in thyroid disease prediction.
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