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An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k ‐means clustering and improved ensemble learning
Author(s) -
Singh Aman,
Mehta Jaydip Chandrakant,
Anand Divya,
Nath Pinku,
Pandey Babita,
Khamparia Aditya
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12526
Subject(s) - computer science , c4.5 algorithm , ensemble learning , boosting (machine learning) , machine learning , cluster analysis , decision tree , artificial intelligence , statistic , k means clustering , cohen's kappa , data mining , mean squared error , naive bayes classifier , support vector machine , statistics , mathematics
In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐ means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐ means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.