Predicting the Success Rate before Liver Transplant using ANN
Aditi Gupta
Aditi Gupta, Assistant Professor, Department of Computer Science, DAV College for Boys, Hathi Gate, Amritsar, Punjab, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on March 02, 2020. | Manuscript published on March 10, 2020. | PP: 1872-1876 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2837039520/2020©BEIESP | DOI: 10.35940/ijitee.E2837.039520
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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: The counterfeit learning models, for example, the artificial neural system, radial basis function and art map have demonstrated a promising application in the medicinal industry. The present work is a comparative examination of the previously mentioned. The consequences of our examination have demonstrated that among Artificial neural system, radial basis function and art map the numeric qualities acquired from ANN were relatively better. Further, the investigation of the exactness among the three chose calculations was found 98.9708%, 97.2556%, and 58.1475% separately. As per writing overview performed, it is clear that most examinations right now got lesser consideration, particularly in India. In view of our discoveries it appears that the ANN could be the best mode to predict the joint stabilities during liver transplantation.
Keywords: Artificial Neural Network (ANN), Radial Basis Function (RBF) and Adaptive Resonance Theory MAP, Liver Transplant(LT).
Scope of the Article: Network Based Applications