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

doi:10.1145/3556223.3556230

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

CDU College of Engineering, IT and Environment

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-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 language	English
Title of host publication	Proceedings of the 10th International Conference on Computer and Communications Management, ICCCM 2022
Publisher	Association for Computing Machinery, Inc
Pages	46-53
Number of pages	8
ISBN (Electronic)	9781450396349
DOIs	https://doi.org/10.1145/3556223.3556230
Publication status	Published - 29 Jul 2022
Event	10th International Conference on Computer and Communications Management, ICCCM 2022 - Okayama, Japan Duration: 29 Jul 2022 → 31 Jul 2022

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	10th International Conference on Computer and Communications Management, ICCCM 2022
Country/Territory	Japan
City	Okayama
Period	29/07/22 → 31/07/22

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10.1145/3556223.3556230

Cite this

Tajmen, S., Karim, A., Hasan Mridul, A., Azam, S., Ghosh, P., Dhaly, A. A., & Hossain, M. N. (2022). A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease. In Proceedings of the 10th International Conference on Computer and Communications Management, ICCCM 2022 (pp. 46-53). (ACM International Conference Proceeding Series). Association for Computing Machinery, Inc. https://doi.org/10.1145/3556223.3556230

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title = "A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease",

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%.",

keywords = "AdaBoost Classifier, Gradient Boosting, K-nearest Neighbors, Liver Disease, Machine Learning, Random Forest",

author = "Shorove Tajmen and Asif Karim and {Hasan Mridul}, Aunik and Sami Azam and Pronab Ghosh and Dhaly, {Al Amin} and Hossain, {Md Nour}",

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year = "2022",

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Tajmen, S, Karim, A, Hasan Mridul, A, Azam, S, Ghosh, P, Dhaly, AA & Hossain, MN 2022, A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease. in Proceedings of the 10th International Conference on Computer and Communications Management, ICCCM 2022. ACM International Conference Proceeding Series, Association for Computing Machinery, Inc, pp. 46-53, 10th International Conference on Computer and Communications Management, ICCCM 2022, Okayama, Japan, 29/07/22. https://doi.org/10.1145/3556223.3556230

A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease. / Tajmen, Shorove; Karim, Asif; Hasan Mridul, Aunik et al.

Proceedings of the 10th International Conference on Computer and Communications Management, ICCCM 2022. Association for Computing Machinery, Inc, 2022. p. 46-53 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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AU - Ghosh, Pronab

AU - Dhaly, Al Amin

AU - Hossain, Md Nour

PY - 2022/7/29

Y1 - 2022/7/29

N2 - 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%.

AB - 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%.

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Tajmen S, Karim A, Hasan Mridul A, Azam S, Ghosh P, Dhaly AA et al. A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease. In Proceedings of the 10th International Conference on Computer and Communications Management, ICCCM 2022. Association for Computing Machinery, Inc. 2022. p. 46-53. (ACM International Conference Proceeding Series). doi: 10.1145/3556223.3556230

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

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