A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease

Shorove Tajmen, Asif Karim, Aunik Hasan Mridul, Sami Azam, Pronab Ghosh, Al Amin Dhaly, Md Nour Hossain

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

Abstract

Liver disease, a life-threatening malice, has become one of the most common diseases in recent years. Our goal is to identify the associated risks early enough through existing preconditions and make efficient predictions of liver disease using cutting edge machine learning models. The dataset is collected from UCI repository. Hybrid classifiers, developed by combining traditional classifiers with Bagging and Boosting methods like Gradient Boosting Boosting Method (GBBM), Random Forest Bagging Method (RFBM), Bagging Method (KNNBM), K-Nearest Neighbors, AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) have been used. K-nearest Neighbors performed the best with Testing Accuracy (TA) of 99.9%.

Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Computer and Communications Management, ICCCM 2022
PublisherAssociation for Computing Machinery, Inc
Pages46-53
Number of pages8
ISBN (Electronic)9781450396349
DOIs
Publication statusPublished - 29 Jul 2022
Event10th International Conference on Computer and Communications Management, ICCCM 2022 - Okayama, Japan
Duration: 29 Jul 202231 Jul 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th International Conference on Computer and Communications Management, ICCCM 2022
Country/TerritoryJapan
CityOkayama
Period29/07/2231/07/22

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