Accurate Liver Disease Prediction with Extreme Gradient Boosting
Sivala Vishnu Murty1, R Kiran Kumar2
1Sivala Vishnu Murty, Dept. of CSE, Aditya Institute of Technology and Management, Tekkali (A.P),India.
2Dr. R Kiran Kumar, Dept of Computer Science, Krishna University, Machilipatnam ,( A.P), India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2288-2295 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8684088619/2019©BEIESP | DOI: 10.35940/ijeat.F8684.088619
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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: Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XG Boost algorithm is doing extremely well for some selected data sets. Building an XG Boost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XG Boost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XG Boost model for the first time for Liver disease prediction and got 99% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.
Keywords: Machine Learning, Classification, Liver Disease, Prediction, Boosting.