Liver Disease Classification using Deep Learning Algorithm
L. Anand1, V. Neelanarayanan2

1L. Anand, Assistant Professor, School of Computing Science and Engineering, Vellore Institute of Technology , Chennai, (Tamil Nadu), India.
2V. Neelanarayanan, School of computing science and Engineering, Vellore Institute of Technology , Chennai, (Tamil Nadu), India. 

Manuscript received on September 19, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5105-5111 | Volume-8 Issue-12, October 2019. | Retrieval Number: L27471081219/2019©BEIESP | DOI: 10.35940/ijitee.L2747.1081219
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Abstract: Data Mining is one of the prevalent elucidating portions of programmed request and distinguishing proof. It involves data mining counts and strategies to examine helpful data. Of late, liver dissents have disproportionately expanded and liver infections are complimenting one of the most human pains in different countries. Early assurance of Liver Disorder is essential for the welfare of human culture. This complaint should be considered sincerely by setting up watchful structures for the early break down and expectation of Liver contaminations. The robotized gathering system suffers with non attendance of precision results when differentiated and cautious biopsy. We propose another model for liver issue request for separating the patient’s helpful, data using ANN algorithm. The remedial records are organized whether there is a believability of essence of disorder or not. This proposed methodology uses extracted features using M-PSO and ANN for classifying the features. The ANN methodology improves the accuracy when appeared differently in relation to existing request computations. This paper focuses classification of selected features for classification.
Keywords: Classification. Algorithm, Methodology Data Mining
Scope of the Article: Algorithm Engineering