Optimized Predictive Models in Health Care Using Machine Learning

5Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease

Thaventhiran Chandrasekar*, Praveen Kumar Karunanithi, K.R. Sekar and Arka Ghosh

School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India

Abstract

Due to the dramatic rise in cigarette smoking, lung cancer is now one of the main causes of death in emerging countries. Accurate diagnosis of the illness described at (LCD) lung cancer disease is essential if persons with lung cancer are to receive appropriate therapy. Artificial neural networks have lately been identified as a machine learning (ML) technique (ANN). In this book, Enhanced WONN- ML for LCD in big data (Weighted Neural Networks using Maximum Likelihood Boosting) are studied. In a unique combination technique for classifier ensembles, optimized Raphson’s Likelihood MR preprocessing model is utilized, and the critical features are extracted to increase the identification of lung cancer disease. Cluster classification and extraction of features make up the two parts of the suggested methodology. In order to shorten the classification time, the primary attributes are determined using an optimized Raphson’s Maximum Likelihood and very less Redundancy preprocessing model. The second stage of the method uses using Enhanced Neural network model with Ensemble Classifier to improve the accuracy of the cancer disease detection and reduce the false alarm rate. Optimized weights for every ensemble classifier’s conclusion are dynamically ...