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2018 | 113 | 117-129
Article title

Evidence Based Diagnosis of Mesothelioma

Content
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EN
Abstracts
EN
The aim of this study is to extract the hidden patterns by using data mining and artificial intelligence techniques. The concept of artificial neural network depends on the idea that we can imitate the working of human brain by making the right links. Artificial Intelligence has always helped in many research areas including medical diagnosis. One of the basic methodologies for training and testing a network by utilizing medical information is discussed here. We have used SAS for analyzing our data and applying feed forward and back propagation mechanism for our diagnosis. The feed forward neural network with back propagation algorithm can be used to identify the diseased ones among different set of admitted individuals. In this paper, we have used multi-layer neural network to achieve the best performance with the minimum epoch (training iterations) and training time.
Year
Volume
113
Pages
117-129
Physical description
Contributors
author
  • Department of Electronics and Communication, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India
author
  • Department of Electronics and Communication, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India
References
  • [1] Wadhonkar Manjusha B., Tijare P.A., Sawalkar S.N., Classification of heart Disease Multilayer Feed Forward Backpropagation. International Journal of Application or Innovation in Engineering and Management (2013) 214-220.
  • [2] Kumar Vinod, Saini Anil, Detection System for lung cancer based on neural network. International Journal of Enhanced Research in Management and Computer Applications (2013) 40-47.
  • [3] Rastogi Astha, Monika Bhalla, A study of Neural Network in Diagnosis of Thyroid Disease. International Journal of Computer Technology and Electronics Engineering (2014) 13-16.
  • [4] Lundin M., Toikkanen S., Burke H. B., Lundin J., Pylkkanen L., Artificial Neural Network Applied to Survival Prediction in Breast Cancer. International Journal of Cancer Research and Treatment, Oncology Vol. 57 (1999) 281-286.
  • [5] Bewal Ritika, Ghosh Aneecia, Chaudhary Apoorva, Detection of Breast Cancer. Journal of Clinical and Biomedical Sciences (2015) 143-148.
  • [6] Zakhmi Rupali, Tuberculosis Disease Forecasting Among Indian Patient. International Journal on Recent and Innovation Trends in Computing and Communication (2016) 180-183.
  • [7] Soltani, Jafarian, Predicting Hypoglycemia in Diabetic Patients. International Journal of Advanced Computer Science and Applications (2016) 89-90.
  • [8] Qeethara Kadhim Al-Shayea, Artificial Neural Networks in Medical Diagnosis, International Journal of Computer Science Issues (2011) 150-154.
  • [9] P. Sudeshna, S. Bhanumathi, Anish Hamlin, Identifying Symptoms and Treatment for Heart Disease from Biomedical Literature Using Text Data Mining, Institute of Electrical and Electronics Engineers (2017) 170-174.
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-6a5b6232-bfab-4aca-b2ca-4e98ccde5050
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