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Naïve Bayes algorithm is used for prediction of disease. This algorithm trains label data sets and for this they must be trained on label data sets. This algorithm works on the basis of prior probability. The prior probability is the probability of disease that is based on its symptoms and is conducted on a data set.

This algorithm is used to predict the disease based on the maximum value between classes and that class will represent its disease or will be selected [19].

ML has contributed a considerable number of disciplines in recent years including healthcare, vision, and natural language processing. There are several machine learning approaches that are analyzed and used for the diagnosis of thyroid disease. The analysis shows that all the papers use different machine learning technologies and show different accuracy. In most research paper, it suggests that logistic regression and decision tree have obtained better accuracy than other algorithms, as shown in Figure 3.4.

Figure 3.4 Analysis of machine learning approach on thyroid.