Enhanced Classifier Accuracy in Liver Disease Diagnosis Using a Novel Multi Layer Feed Forward Deep Neural Network
Sivala Vishnu Murty1, R Kiran Kumar2
1Sivala Vishnu Murty, Dept of CSE, Aditya Institute of Technology and Management, Tekkali (A.P) India.
2Dr. R Kiran Kumar, Dept. of Computer Science, Krishna University, Machilipatnam, Andhra Pradesh, India.
Manuscript received on 06 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript published on 30 July 2019 | PP: 1392-1400 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2047078219/19©BEIESP | DOI: 10.35940/ijrte.B2047.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification techniques are often used for predicting Liver diseases and assist doctors in early detection of liver diseases. As per studies in the past and our experiments, conventional classification algorithms are found to be less accurate in predicting liver diseases. Therefore, there is a need for sophisticated classifiers in this area. For many medical applications, including Liver Diseases, Deep Neural Networks (DNNs) are used but the accuracies are not satisfactory. Deep Neural Network training is a time taking procedure, particularly if the hidden layers and nodes are more. Most of the times it leads to over fitting and the classifier does not perform well on unseen data samples .We, in this paper, tuned a Multi Layer Feed Forward Deep Neural Network (MLFFDNN) by fitting appropriate number of hidden layer and nodes, dropout function after each hidden layer to avoid over fitting, loss functions, bias, learning rate and activation functions for more accurate liver disease predictions. We used a balanced data set containing 882 samples. The data is collected from north coastal districts of Andhra Pradesh hospitals, India. The training process is carried out for 400 epochs and finally It is .observed that our model exhibited 98% accuracy at epoch 363 which is more than the performance of Neural Network models tuned till now by machine learning researchers and also some regularly used classification algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision Tree, Random Belief Networks and Alternating Decision Trees (ADT) .
Index Terms: Classification, Deep Neural Networks, Feed Forward Neural Network, Liver Disease, Prediction.
Scope of the Article: Classification