Data Mining Algorithm to Predict Systemic Lupus Erythematosus (SLE)

Data mining is used for physicians to identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of information generated by healthcare transactions are too advanced and voluminous which is complex to be processed and analyzed in traditional ways. The main objective of the book focuses on developing an optimal cluster BasedClassification algorithm (OCBC), a modern data mining algorithm to predict Systemic LupusErythematosus disease. The accuracy of the algorithm is shown by building the Systemic LupusErythematosus prediction tool to predict the disease in the early stage Clusterbased Classification using an optimal algorithm (OCBC). The algorithms, namely, ID3, C4.5, J48 are selected as the base algorithms to Compareth accuracy, specificity, sensitivity, precision, recall and F-measure, Kappa statistics, etc., with the proposed OCBC algorithm. Many kinds of research have been conducted to compare the accuracy of the OCBC algorithm on the same dataset, and the result shows that OCBC outperforms ID3 J48 and algorithms. The accuracy of the OCBC further improves after clustering the dataset.