Open Access

ARTICLE

Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

M. L. Maruthi Prasad^*, R. Santhosh

Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

* Corresponding Author: M. L. Maruthi Prasad. Email:

Intelligent Automation & Soft Computing 2024, 39(1), 1-13. https://doi.org/10.32604/iasc.2023.036628

Received 07 October 2022; Accepted 13 December 2022; Issue published 29 March 2024

Abstract

This research concentrates to model an efficient thyroid prediction approach, which is considered a baseline for significant problems faced by the women community. The major research problem is the lack of automated model to attain earlier prediction. Some existing model fails to give better prediction accuracy. Here, a novel clinical decision support system is framed to make the proper decision during a time of complexity. Multiple stages are followed in the proposed framework, which plays a substantial role in thyroid prediction. These steps include i) data acquisition, ii) outlier prediction, and iii) multi-stage weight-based ensemble learning process (MS-WEL). The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier model. Various classifiers are merged to handle the issues identified in others and intend to enhance the prediction rate. The proposed model provides superior outcomes and gives good quality prediction rate. The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches. The model gives a prediction accuracy of 97.28% accuracy compared to other models and shows a better trade than others.

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Keywords

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