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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