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An advanced deep learning approach for predicting liver cirrhosis

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dc.contributor.author Sohag, Sohanur Rahman
dc.date.accessioned 2024-08-27T09:11:51Z
dc.date.available 2024-08-27T09:11:51Z
dc.date.issued 2024-01-26
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/13252
dc.description.abstract This study provides an advanced deep learning method for predicting liver cirrhosis using a Kaggle dataset of 615 entries with a target attribute "Liver Cirrhosis Status" that classifies answers as 'Yes' (75 cases) or 'No' (540 cases). Complete Data collection, Preprocessing, Model selection, Training, and Evaluation are all part of the suggested methodology. The trial findings reveal that the Artificial Neural Network (ANN) outperforms other categorization algorithms with its high accuracy of 98.01%. This discusses the model's extraordinary ability to identify detailed patterns in the medical dataset, proving its potential for accurate liver cirrhosis prediction. The ANN's achievement shows the utility of advanced deep learning techniques in medical testing, particularly for complex problems such as liver cirrhosis prediction. The group methods RandomForestClassifier and AdaBoosting, as well as SVC's unfair capabilities, all performed well, suggesting they are suitable for capturing subtle relationships within the dataset. These findings add to the growing body of knowledge about the use of complex neural networks created in healthcare, paving the way for better patient outcomes through early and exact prediction of liver cirrhosis. The findings of the study have important implications for continuing efforts to improve medical diagnostic capacities through the use of modern artificial intelligence technologies. en_US
dc.publisher Daffodil International University en_US
dc.subject Medical Diagnosis en_US
dc.subject Health Informatics en_US
dc.subject Artificial Intelligence (AI) en_US
dc.subject Classification Algorithms en_US
dc.subject Liver Cirrhosis en_US
dc.subject Deep Learning en_US
dc.title An advanced deep learning approach for predicting liver cirrhosis en_US
dc.type Other en_US


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