Title: BLDA-CSWDT autoimmune thyroid disease risks predictive model using machine learning and deep feature extraction techniques

Authors: Nagavali Saka; S. Murali Krishna

Addresses: Jawaharlal Nehru Technological University Anantapur (JNTUA), Anantapur, Andhra Pradesh, India ' Department of Computer Science and Engineering, Sri Venkateswara College of Engineering (SVCE), Tirupati, Andhra Pradesh, India

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.

Keywords: autoimmune thyroid disease; deep MLP; cuckoo search optimisation; LDA; weighted decision tree; Bayes linear discriminant analysis; B-LDA; cuckoo search based weighted decision tree; CSWDT.

DOI: 10.1504/IJBET.2023.133791

International Journal of Biomedical Engineering and Technology, 2023 Vol.43 No.2, pp.185 - 206

Received: 21 Apr 2022
Accepted: 12 Nov 2022
Published online: 03 Oct 2023 *

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