Effective Detection of Compensated Cirrhosis Using Machine Learning

Sarvestany, Soren Sabet; Kwong, Jeffrey C.; Azhie, Amirhossein; Dong, Victor; Cerocchi, Orlando; Ali, Fuad Ahmed; Karnam, Ravikiran S; Kuriry, Hadi; Shengir, Mohamed; Candido, Elisa; Duchen, Raquel; Sebastiani, Giada; Patel, Keyur; Goldenberg, Anna; Bhat, Mamatha

doi:10.2139/ssrn.3705267

Cited by 1 publication

(1 citation statement)

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“…The best accurate prediction may be achieved by building the method of assessing chronic liver disease using an improved feed-forward neural network with back-propagation that is combined with an improved ant colony optimization [9]. Machine learning can be used to diagnose wellcompensated cirrhosis across various liver disease etiologies, and the Ensemble algorithm outperforms all other machine learning techniques [10]. A study on machine learning methods for detecting liver illness done by [11].…”

Section: Literature Surveymentioning

confidence: 99%

Multi Perceptron Neural Network and Voting Classifier for Liver Disease Dataset

Anthonysamy,

Babu

2023

IEEE Access

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The liver is one of the most significant organs in the human body. We can predict liver disease in a patient at an early stage based on previously predicted values using data from patients with abnormal liver function. Which helps the doctors to make a diagnosis. In this paper, the liver function test is analyzed for predicting liver disease, where the input of the patient's details and output data are passed into various classifiers such as Support Vector Machine, K-Nearest Neighbor, Hard Voting Classifier, and Deep Neural Network Multilayer Perceptron Techniques. Model Evaluation Criteria such as the Confusion Matrix, Precision Score, Recall, Accuracy, Specificity, and F-score are used to determine the best model. A dataset of 583 individuals suffering from liver disease is analyzed and we found that Hard Voting Classifier (HVC) is the best for this dataset. Additionally, this Voter Classifier prediction algorithm gives higher accuracy, which will help to diagnose liver disease.

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Section: Literature Surveymentioning

confidence: 99%