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All Journal International Journal of Public Health Science (IJPHS)
Srirama Murthy, Konakanchi Venkata Subrahmanya
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Health Professions

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Comparative analysis of deep learning models for various nonalcoholic fatty liver disease datasets Srirama Murthy, Konakanchi Venkata Subrahmanya; Shankar, Reddy Shiva; Pradhan, Samarendra Narayana; Mohanty, Bhabodeepika; Rao, Veeranki Venkata Rama Maheswara
International Journal of Public Health Science (IJPHS) Vol 13, No 4: December 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v13i4.23891

Abstract

Fatty liver disease is caused by increased liver buildup or weight above 5-10%. This disorder is widespread in people with diabetes, overweight persons, and metabolic syndrome patients. Clinical decision support systems can improve liver failure diagnosis and prediction to reduce this situation. Many liver failure models have drawbacks, and liver failure prediction is still a problem. This work uses four large open-access critical care patient datasets to create and verify liver failure risk prediction models. This study aims to construct a clinically applicable diagnostic and predictive model that evaluates the probability or risk of liver failure in intensive care unit (ICU) patients using extreme gradient boosting (XGBoost), artificial neural networks (ANN), multi-layer perceptron (MLP), Modular Neural Network (MNN), and generalized feed forward (GFF). We evaluated performance metrics using these models: accuracy, sensitivity, specificity, and predictive accuracy.
Co-Authors Mohanty, Bhabodeepika Pradhan, Samarendra Narayana Rao, Veeranki Venkata Rama Maheswara Shankar, Reddy Shiva