Improvised prophecy using regularization method of machine learning algorithms on medical data

Patients with thyroid disease (TD) boast continuously increasing because of excessive growth of thyroid gland and its hormones. Automatic classification tools may reduce the burden on doctors. This paper evaluates the selected algorithms for predicting thyroid disease diagnoses (TDD). The algorithms considered here are regularization methods (RM) of machine learning algorithms (MLA). The analysis report generated by the proposed work suggests the best algorithm for predicting the exact levels of TDD. This work is a comparative study of MLA on UCI thyroid datasets (UCITD). The developed system deals with RM i.e., ridge regression algorithm (RRA) & least absolute shrinkage and selection operator algorithm (LASSO). The above algorithms personage produce at most 79% accuracy by RRA and 98.99% accuracy by LASSO. Thus, this paper shows the importance of LASSO, along with an example for parameter generation. The decisive factors (DF) also suggest the accuracy rate of LASSO is much better when compared with RRA.