A New Approach For Prediction of Lung Carcinoma Using Back Propogation Neural Network with Decision Tree Classifiers

In this paper an investigation was made to examine the lung tumour expectation utilizing classification algorithms, for example, Back Propagation Neural Network and Decision Tree. At first 20 tumour and non-disease patients' examples information were gathered with 30 qualities, pre-prepared and dissected utilizing classification algorithms and later a similar methodology was actualized on 50 occurrences (50 Cancer patients and 10 non growth patients). The informational indexes utilized as a part of this examination are taken from UCI data sets for patients affected by lung cancer and Michigan Lung Cancer patient's informational index.The principle point of this paper is to give the prior notice to the clients and to quantify the execution investigation of the classification algorithms utilizing WEKA Tool. Test comes about demonstrate that the previously mentioned calculation has promising outcomes for this reason with the general forecast exactness of 94% and 95.4%, separately. Another way to deal with identifies the lungs tumour by Decision tree and BPNN calculation will give viable outcome as contrast with other calculation. The proposed framework will improve the execution of prediction and classification.