The objective of this study is to diagnose the patients with hypertension using Artificial Neural Network. Learning rate and momentum coefficient are the parameters used to construct network. The values of parameters are selected randomly and then the values are increased or decreases in every step iteratively. The topologies like Number of hidden layers, number of hidden nodes and the type of activation functions are also used to construct network. In order to improve the accuracy of the network, the result obtained by Back Propagation algorithms has been fused using Proportional Conflict Redistribution (PCR) rule. The output of the Back Propagation networks is considered as the primary diagnosis results and fused this result with Proportional Conflict Redistribution (PCR) rule to get the final results. Fusion method proposed in this paper is to enhance the target performance and reduce the uncertainty level. The experimental result shows that the fusion method produced higher accuracy and lower level of uncertainty.

Hypertension, Fusion, Back Propagation, uncertainty, Diagnosis

[1] John Trinder, Mahmoud Salah, “Combining Statistical and Neural classifiers using Dempster-Shafer Theory of Evidence for Improved Building Detection,” ARSPC, Alice Springs, pp. 13-17, 2010.
[2] Zhang Tao and Qi Yong-Qi, “Uncertainty Analysis of Integrated Navigation Model for Underwater Vehicle,” Research Journal of Applied Sciences, Engineering and Technology, Vol.6, pp. 1614-1620, 2013.
[3] Cleber Zanchettin, Teresa B. Ludermir, and Leandro Maciel Almeida, “Hybrid Training Method for MLP: Optimization of Architecture and Training”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 41, No. 4, pp. 1097-1109, 2011.
[4] Bengio. Y, Mori Renato, Flammia. G Kompe. R, “Global Optimization of A Neural Network-Hidden Markov Model Hybrid”, IEEE Transaction on Neural Networks, 1992, Vol.3, No. 2, pp. 252-259.
[5] Behera L, Kumar S, Patnaik A, “On Adaptive Learning Rate that Guarantees Convergence in Feed Forward Networks,” IEEE Transaction on Neural Networks, Vol. 17, pp. 1116-1125, 2006.
[6] Summit Goyal, Gyanendra Dumar Goyal, “Heuristic Machine Learning Feedforward Algorithm for Predicting Shelf Life of Processed Cheese”, International Journal of Basic and Applied Science, 1(4): 458-467, 2012.
[7] Somasundaram. R.S, Nedunchezhian. R, “Evaluation of Three Simple Imputation Methods for Enhancing Preprocessing of Data with Missing Values”, International Journal of Computer Applications, 2011.
[8] Rehman M. Z., Nawi N. M., “Improving the Accuracy of Gradient Descent Back Propagation Algorithm (GDAM) on Classification Problems”, International Journal on New Computer Architectures and Their Applications, pp. 861-870, 2011.
[9] Yu L., Wang S., and Lai K., “An Adaptive BP Algorithm with Optimal Learning Rates and Directional Error Correction for Foreign Exchange Market Trend Prediction”, Advances in Neural Networks - Springer, Berlin Heidelberg, pp. 498-503, 2006.
[10] Rui Quan, Shuhai Quan, Lang Huang, Changjun Xie and Qihong Chen, “Information fusion in Fault Diagnosis for Automotive Fuel Cell System Based on D-S Evidence Theory”, Journal of Computational Information Systems, pp. 97-105, 2011.
[11] Arka Ghosh and Mriganka Chakraborty, “Hybrid Optimized Back propagation Learning Algorithm for Multi-layer Perceptron,” International Journal of Computer Applications, Vol. 57, pp. 1-6, 2012.
[12] Ludmila I and Kuncheva, “A Theoretical Study on Six Classifier Fusion Strategies,” IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 2. Feb 2002, Vol. 24, pp. 281-286.
[13] Chen yi, Huang qing and Chen Yanlan, “An Improve Information Fusion Algorithm Based on BP Neural Network and D-S Evidence Theory,” IEEE Third International Conference on Digital Manufacturing & Automation, pp. 179-181, 2012.
[14] Aiman S. Gannous, Younis R. Elhaddad (2011), “Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques”, World Academy of Science, Engineering and Technology. 50: 124-128.
[15] Atthapol Ngaopitakkul and Chaiyan Jettanasen, “Selection of Proper Activation Functions in Back-Propagation Neural Networks Algorithm for Identifying the Phase With Fault Appearance in Transformer Windings”, international journal of innovative computing, informational and control, Vol.8, Issue.6, pp. 4299-4198, 2012.
[16] Bekir Karlik and Vehbi Olgac. A, “Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks”, International Journal of Artificial Intelligence And Expert Systems, Vol.1, Issue.4, pp.111-122, 2010.
[17] Bushra M. Hussan, Ghaida al-Suhal, “Studying the Impact of Handling the Missing Values on the Dataset on the Efficiency of Data Mining Techniques”, Basrah Journal of Science, Vol.2, pp. 128-141, 2012.
[18] Koushal Kumar and Abhishek, “Artificial Neural Networks for Diagnosis of Kidney Stones Disease”, International Journal of Information Technology and Computer Science, Vol.7, pp. 20-25, 2012.
[19] B. Sumathi, “Neural Networks Evidence Combination for the Diagnosis of Hypertension”, National Conference on Emerging Trends in Information and Computer Technology NCETICT- at Kingston Engineering College, 2013.
[20] B. Sumathi, “Increasing the Rate of Convergence of Back Propagation Algorithm Using Dempster-Shafer Theory for the Diagnosis of Hypertension”, In Proceeding of the 2016 National Conference on Advanced Trends in Information Technology, India, pp.70, 2016.
[21] Deepali Kamath, Anupama Ajith, Kavita Pujari, Praveena Kumari MK , “A Survey on Data Mining Techniques Applied on Cardiovascular Diseases and Cancer, Diagnosis and Prognosis”, International Journal of Computer Science and Engineerin, Vol.6, Issue.8, pp. 544-550, 2018.
[22] B. Sumathi, “Pre-Diagnosis of Hypertension using Artificial Neural Network”, Global journal of Computer Science and Technology, Vol. 11, Issue. 2, pp. 43-47, 2011.
[23] Praveen Tripathi, R. Belwal, A.K.Bhatt, “Assessment of Apple Quality based on Scaled Conjugate Gradient Technique, using Artificial Neural Network Model”, International Journal of Computer Science and Engineerin, Vol.6, Issue.7, pp. 103-108, 2018.
[24] S. Ramana, S. Sabitha, R. Senthil Kumar, T. Senthil Prakash, “Atmospheric Change on the Geographical Theme Finding Of Different Functions on Human Mobility”, IJSRCSE, Vol.6 , Issue.2, pp.134-151, Apr-2018.