Association Rule Extraction and Correlation Analysis Framework for Dominant Feature Selection in Thyroid Disease Data

Thyroid related illnesses are one of the most common among disorders of the endocrinal glands. This paper is an attempt at extracting the principal features of thyroid disease dataset. These relevant features trained on a suitable machine learning model can successfully diagnose thyroid diseases in a machine learning pipeline. In this pa-per, we propose a framework of iterative Association Rule Mining and Correlation Analysis, which integrates associa-tion rule mining algorithm and subsequent correlation-based analysis of the result set to extract dominant features in the dataset. Apriori and FP Growth algorithms, the two most popular association rule mining algorithms, were used to extract the major attributes which causes thyroid related sicknesses. The association rule mining is used in dimen-sionality reduction of the dataset. We have used a thyroidi-tis dataset for this study, which is first preprocessed to pre-pare it for association rule mining. Preprocessing is a signif-icant step in our method. Next step is the splitting of the dataset into n data subsets. The Apriori and FP growth algorithms are applied on these multiple data subsets to create frequent k-itemsets. The most relevant of the associa-tion rules are selected by a majority voting from the result set generated after the application of association rule mining. The result set, comprising of the features which are selected as the principal attributes are again filtered using correlation analysis. The results show that gender factor is the most dominant of all features. It shows a predominance in association of women with Thyroid diseases than men. In the proposed method, the dataset undergoes a two-step process of refinement and consequently, association rules generated gives valuable insights into the thyroid related disease’s causative factors. The experimental results show that the performance of the proposed framework helps in principal feature selection, and thus can be used in dimen-sionality reduction of a dataset in the pre-processing step of classification and prediction of thyroid diseases. This framework can be used in the pipeline of thyroid disease prediction systems.