TY - GEN
T1 - A Machine Learning based Proposition for Automated and Methodical Prediction of Liver Disease
AU - Tajmen, Shorove
AU - Karim, Asif
AU - Hasan Mridul, Aunik
AU - Azam, Sami
AU - Ghosh, Pronab
AU - Dhaly, Al Amin
AU - Hossain, Md Nour
N1 - Publisher Copyright:
© 2022 ACM.
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%.
KW - AdaBoost Classifier, Gradient Boosting, K-nearest Neighbors
KW - Liver Disease
KW - Machine Learning, Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85140769401&partnerID=8YFLogxK
U2 - 10.1145/3556223.3556230
DO - 10.1145/3556223.3556230
M3 - Conference Paper published in Proceedings
AN - SCOPUS:85140769401
T3 - ACM International Conference Proceeding Series
SP - 46
EP - 53
BT - Proceedings of the 10th International Conference on Computer and Communications Management, ICCCM 2022
PB - Association for Computing Machinery, Inc
T2 - 10th International Conference on Computer and Communications Management, ICCCM 2022
Y2 - 29 July 2022 through 31 July 2022
ER -