Liver disease detection using machine learning techniques

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dc.contributor.authorBhupathi, Den_US
dc.contributor.authorTan, CN-Len_US
dc.contributor.authorTirumula, SSen_US
dc.contributor.authorRay, SKen_US
dc.coverage.spatialChristchurch, New Zealanden_US
dc.date.available30/11/2022en_US
dc.date.finish-date7/10/2022en_US
dc.date.issued30/11/2022en_US
dc.date.start-date4/10/2022en_US
dc.description.abstractAround a million deaths occur due to liver diseases globally. There are several traditional methods to diagnose liver diseases, but they are expensive. Early prediction of liver disease would benefit all individuals prone to liver diseases by providing early treatment. As technology is growing in health care, machine learning significantly affects health care for predicting conditions at early stages. This study finds how accurate machine learning is in predicting liver disease. This present study introduces the liver disease prediction (LDP) method in predicting liver disease that can be utilised by health professionals, stakeholders, students and researchers. Five algorithms, namely Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbors (K-NN), Linear Discriminant Analysis (LDA), and Classification and Regression Trees (CART), are selected. The accuracy is compared to uncover the best classification method for predicting liver disease using R and Python. From the results, K-NN obtains the best accuracy with 91.7%, and the autoencoder network achieved 92.1% accuracy, which is above the acceptable level of accuracy and can be considered for liver disease prediction.en_US
dc.description.confidentialTRUEen_US
dc.identifier.citationProceedings of the 13th Annual CITRENZ Conference: Unifying Educational Delivery and Collaborating Towards Technical Excellence, 2022en_US
dc.identifier.elements-id458308
dc.identifier.harvestedMassey_Dark
dc.identifier.urihttps://hdl.handle.net/10179/17847
dc.relation.isPartOfProceedings of the 13th Annual CITRENZ Conference: Unifying Educational Delivery and Collaborating Towards Technical Excellenceen_US
dc.rights(c) The author/s (CC BY 4.0)english
dc.sourceProceedings of the 13th Annual Conference of Computing and Information Technology Education and Research in New Zealanden_US
dc.titleLiver disease detection using machine learning techniquesen_US
dc.typeConference Paper
pubs.notesNot knownen_US
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/Massey Business School
pubs.organisational-group/Massey University/Massey Business School/School of Management
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