Applying Ensemble Techniques for the Prediction of Alcoholic Liver Cirrhosis

Title

Applying Ensemble Techniques for the Prediction of Alcoholic Liver Cirrhosis

Creator

Vinutha M.R.; Chandrika J.; Krishnan B.; Kokatnoor S.A.

Description

More than fifty percent of all liver cognate deaths are caused by alcoholic liver disease (ALD). Excessive drinking over the time leads to alcohol-related steatohepatitis and fatty liver, this in turn can lead to alcoholic liver fibrosis (ALF) and in due course alcohol-related liver cirrhosis (ALC). Detecting ALD at an early stage will reduce the treatment cost to the patient and reduce mortality. In this research, a two-step model is developed for predicting the liver cirrhosis using different ensemble classifiers. Among 41 features recorded during data collection, only 15 features arefound to be effective determinants of the class variable. The proposed stacked ensemble technique for ALD prediction is compared with other ensemble models such as random forest, AdaBoost, and bagging. Through experimentation, it is observed that the proposed model with XGBoost and decision tree as base models and logistic regression as Meta model exhibits prediction accuracy of 93.86%. The prediction accuracy of theproposed stacked ensemble technique is 0.2% better in prediction accuracy and 0.3% reduced error rate in comparison with random forest classifier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Source

Lecture Notes in Networks and Systems, Vol-462, pp. 433-445.

Date

2022-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Alcoholic liver disease; Ensemble classifier; Liver cirrhosis; Machine learning; Random forest; Stacked ensemble classifier; XGBoost

Coverage

Vinutha M.R., Department of ISE, Malnad College of Engineering, Hassan, 573201, India; Chandrika J., Department of ISE, Malnad College of Engineering, Hassan, 573201, India; Krishnan B., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981192210-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-19-2211-4_39

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135054733&doi=10.1007%2f978-981-19-2211-4_39&partnerID=40&md5=3d449fa1b83f044f9de0eb133ff157f9