Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/1951 2018 2018 Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India Maruthi Maruthi Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India Santhosh. R. The studies’ primary aim is to help the research scholars as a source who would like to research in the thyroid disease detection region. UC Irvin knowledge discovery provides databases files for the machine learning archives' thyroid dataset. Here, a random vector network model (RVNM) is proposed to perform classification tasks. The proposed model integrates the prior dataset information regarding the samples to train the more effective classifier. This cascaded random vector network model helps in thyroid disease prediction. The evaluation process is performed to predict and determine the respective performance concerning accuracy. The intuition is provided in this research, like forecasting the thyroid disease; it also calls attention to the process of using a Randomized Vector Network Model (RVNM) as a medium for classification. 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 96.1% accuracy compared to other models and shows a better trade than others. 2023 2023 132 144 10.54216/FPA.120211 https://www.americaspg.com/articleinfo/3/show/1951