EANNMHO – A Novel Ensemble Based Technique for Liver Cirrhosis Detection

Vinutha M.R, Chandrika J

Abstract


In today's fast moving world, Liver Cirrhosis is considered as an aspect having substantial significance both at the national level and international level. The preliminary interest of medical science is to develop a constructive method to predict the Liver Cirrhosis at an early stage. The extreme heterogeneous nature of the disease along with non-standardized treatment makes its management a complex issue. Though medical modalities assess the disease, patients responses creates variation in them. Machine Learning techniques have been used in medical prognosis as it helps physicians to assess the disease faster. Taking this hint and contemplating the troubles faced by the physicians in diagnosing Liver Cirrhosis we have proposed a novel technique called EANNMHO.EANNMHO is a hybrid technique involving EANN-Ensemble Artificial Neural Network and MHO- Modified Harris Hawk Optimization and initially missing values are imputed using K-Nearest Neighbor. The Proposed model when evaluated against other ML techniques produces conclusive results.


Keywords


Liver Cirrhosis, SVM, Artificial Neural Network, Modified Harris Hawks Optimization , K-Nearest Neighbor

Full Text:

PDF



International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
Creative Commons License
Indexing:
Scopus logo Clarivate Analyatics ESCI logo IET Inspec logo DOAJ logo DBLP logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo