Performance Analysis of Classifier Models to Predict Thyroid Disease - Saktheeswari, M; Balasubramanian, T (International Journal of Computer Sciences and Engineering, 2025)
Machine Learning Algorithm aims at providing computational method for accumulating, changing and updating knowledge in health care systems. In particular learning mechanism will assist us to procure knowledge from the data set. The classification of machine learning algorithm is used not only to detect diseases, but also measure better fidelity. This article emphasizes on codification of disease symptoms on thyroid disease among the public. Thyroid disease is rampant worldwide. There are feasibility of thyroid disease and disorder including thyroiditis and thyroid cancer. We used 7200 sample thyroid dataset from the University of California Irvine Machine Learning Repository, a large and highly imbalanced dataset that comprises both discrete and continuous attributes. In this work, we collate machine learning classifiers such as Logistic Regression, Linear Discriminant Analysis, Naive Bayes, k-Nearest Neighbours, Classification and Regression Tree, Support Vector Machine using python to classify the disease symptoms. This work is carried out using different classifiers to achieve more verisimilitude. The selected algorithms are evaluated using five performance metrics namely accuracy, sensitivity, specificity, F1-score and kappa, and also estimated from the confusion matrix produced by the selected classifier