Machine Learning Techniques in Analysis and Prediction of Liver Disease
Author(s):
Harshitha R, Navya N C, Nitin Ravichander, Dr. Dattatreya P Mankame
Keywords:
Classification models, Data visualization Feature selection, Liver disease, Machine learning, SVM Algorithm
Abstract
Machine Learning features a strong potential in automated diagnosis of varied diseases. The liver plays a very important role in life which supports the removal of toxins from the body. With the recent upscale in various liver diseases It's necessary to identify liver disease at a preliminary stage. India having a population of 1.33 billion is the second most populated country within the world and every year millions are diagnosed with liver diseases. Some sorts of liver diseases are Jaundice, Hepatitis (A, B, C), Non-Alcoholic liver disease Diseases (NAFLD). These are caused by the consumption of alcohol, contaminated food, and certain factors such as obesity. Thus we would prefer a system that's reliable and may predict the symptoms of liver diseases. This technique predicts liver diseases using the patient’s data and by using Machine Learning algorithms. From the experiments and comparative analysis, it increases classification accuracy and also leads to reduction in classification time and hence it aids for prediction of liver disease more efficiently. The performance is measured in terms of accuracy, auc score, precision, recall and f-measure. Several classification algorithms are used and based on the classification report and performance, the best model is chosen and employed to classify liver patients (Liver patient or not liver patient).
Article Details
Unique Paper ID: 152030

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 209 - 214
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