Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

Data mining is discovering interesting knowledge, namely associations, patterns, changes, significant structures, and anomalies, from enormous quantities of data stored in data warehouses, databases, or other data repositories. Association is the discovery of association correlations or relationships amongst a set of items. Associate rule mining is a method for identifying frequent patterns, correlations, associations, or causal structures in data sets found in numerous relational databases, transactional databases, and other data repositories. The algorithm performed is a fundamental process for mining association rules called a priori. In the healthcare field, association rules are useful as they offer the possibility to build important knowledge bases, conduct intelligent diagnoses, and extract invaluable information automatically and quickly. This study develops a Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System (MLARMC-HDMS). The presented MLARMC-HDMS technique performs both classification and ARM processes. Initially, the MLARMC-HDMS technique employs the chimp optimization algorithm-based feature selection (COAFS) technique for attribute selection. Next, stochastic gradient descent with a multilayer perceptron (SGD-MLP) model is applied for the classification process. Moreover, the Apriori algorithm is used to determine the relationship between the attributes. To illustrate the enhanced performance of the presented MLARMC-HDMS model, a detailed experimental validation is performed on a benchmark medical dataset. The experimental values indicated the enhancements of the MLARMC-HDMS model over other methods with maximum accuracy of 99.75%.