Classification of Healthcare Data Using T-BMSVM in Big Data

Abstract

With the fast pace in collating big data healthcare framework, a streamlining of machine learning algorithms together with apache spark has been designed for effective classification of images and stages of lung cancer to the greatest extent. We experiment on a combination of binary classification (SVM-non linear SVM with Radial Basis Function RBF) and Multi-class classification (WTA-SVM winner-takes-all with support vector machine) with threshold technique (T-BMSVM) to classify nodules into malignant or benign nodules and also their malignancy levels respectively. We have argued for handling and processing large sizes of data sets as sputum cell images in the field of classification using the map-reduce framework in Pyspark, which works better with Apache spark. The outsourced outcomes reveal that classification works better with Multi-class SVM, therefore, may be considered as a promising tool to diagnose the stages of nodules. Also, Scalability and convergence analysis embed to prove the improving results of multi-class classification than SVM.