Lung cancer disease is the most widely recognized deadly disease in the world for loss of life. Throughout this research, Electronic Health Records (EHRs) textual data are investigated and survivability rates for lung cancer affected patients are predicted. If the patients are survivable more than one year, chemotherapy treatment can be started for those patients. This research paper examines an effective Batch Size-Optimizer based Deep Neural Network (Op-DNN) classifier framework model, which is developed to predict the patient’s survivability based on status dead or alive. Considering only the patients who are alive, prediction is done to know how many months the patients will survive by Op-DNN regression technique. Here the textual data set is classified and processed in batches for each iteration. The errors generated from the original classification of the first batch size is fed back to the Op-DNN algorithm for further iterations with the reduced error loss that are free from underfitting and overfitting. The proposed method is compared with various parameters for Machine learning classifier algorithms demonstrating that the Op-DNN model has achieved better accuracy.

Lung Cancer, Diabetes, Survivable Rate, Artificial Neural network, DNN, Op-DNN, Classifier, SVM, NBs, C4.5, Optimizer, Adam, Relu, Epoch, Batchsize,Op-DNN Regression

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