An efficient technique for disease prediction by using enhanced machine learning algorithms for categorical medical dataset

Abstract

In the 20th century, it is evident that there is a massive evolution of chronic diseases. The data mining approaches are beneficial in making some medicinal decisions for curing diseases. But medical data may consist of a large number of data, which makes the prediction process very difficult. Also, in the medical field, the database may involve both small and extensive datasets. This creates the study of a complex one for disease prediction mechanism. Hence, in this paper, we intend to improvise the machine learning approaches for disease prediction of both large and small datasets. Among the various machine learning procedures, classification and clustering methods play a significant role. Therefore, we are planned to improvise the machine learning algorithms. Then we introduced the enhanced machine learning algorithms for classification and clustering technique in this work for obtaining better accuracy results for disease prediction. In this proposed method, a process of preprocessing is involved, which follows by Eigen vector extraction, feature selection, and classification. Further, the most suitable features are selected with the use of Multi-Objective based Ant Colony Optimization (MO-ACO) from the extracted features for increasing the accuracy of classification and clustering. Here we have shown the novelty in every stage of the implementation, such as feature selection, feature extraction, and the final prediction algorithm stage. The proposed method is compared with the existing technique on the measure of precision, Normalized Mutual Information, execution time, recall and accuracy. Here we conclude with the solution having more accuracy for all kind of categorical datasets which includes both small and large scale datasets.