Chronic Liver Disease Prediction Analysis Based on the Impact of Life Quality Attributes
Sivakumar D1, Manjunath Varchagall2, Ambika L G3, Usha S4
1Sivakumar D, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
2Manjunath Varchagall, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
3Ambika L G, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
4Usha S, Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore (Karnataka), India.
Manuscript received on 15 May 2019 | Revised Manuscript received on 19 May 2019 | Manuscript Published on 23 May 2019 | PP: 2111-2117 | Volume-7 Issue-6S5 April 2019 | Retrieval Number: F13790476S519/2019©BEIESP
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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: In the fast developing world living style of individuals are twisted a great deal and will impact the strength circumstance of people. The World Health Organization (WHO) insights give data that the endless liver maladies have a massive arrangement of enthusiasm for restorative research attributable to its effect on individual strength. This research study is intended to recognize and analyze the human life quality attributes in forecasting the chronic liver disease with machine learning techniques. The information gathered from the scope of residents living in Bangalore district and the information realistic inside online information storehouse are the contribution to this examination investigation. Classification methods K-means clustering calculation and the C4.5decision tree approaches are utilized in this examination and the accuracy, recall, precision and the F-measures are the measures evaluated to demonstrate the outcomes with the distinctive error measures RMSE, MAE and Kappa measurement esteems. This interminable liver illness forecast process is demonstrated with a precision of 94.36 rates in C4.5 calculation and 93.7 rates with K-implies grouping procedures.
Keywords: Prediction, Prevention, Chronic Liver Disease, K-means Clustering, C4.5 Decision Tree, Life Quality Attributes.
Scope of the Article: Predictive Analysis