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All Journal International Journal of Public Health Science (IJPHS)
Pradhan, Samarendra Narayana
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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.
Evaluation of stress based on multiple distinct modalities using machine learning techniques Pradhan, Samarendra Narayana; Shankar, Reddy Shiva; Barik, Shekharesh; Mohanty, Bhabodeepika; Rao, Venkata Rama Maheswara
International Journal of Public Health Science (IJPHS) Vol 13, No 2: June 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v13i2.23577

Abstract

Nowadays, one of the most time-consuming and complex study subjects is predicting working professionals' stress levels. It is thus crucial to estimate active professionals' stress levels to aid their professional development. Several machine learning (ML) and deep learning (DL) methods have been created in earlier articles for this goal. But they also have drawbacks, such as increased design complexity, a high rate of misclassification, a high incidence of mistakes, and reduced efficiency. Considering these issues, the objective of this study is to make a prognosis about the stress levels experienced by working professionals by using a cutting-edge deep learning model known as the convolutional neural networks (CNN). In this paper, we offer a model that combines CNN-based classification with dataset preprocessing, feature extraction, and optimum feature selection using principal component analysis (PCA). When the raw data is preprocessed, duplicate characteristics are eliminated, and missing values are filled.
Co-Authors Barik, Shekharesh Mohanty, Bhabodeepika Rao, Veeranki Venkata Rama Maheswara Rao, Venkata Rama Maheswara Shankar, Reddy Shiva Srirama Murthy, Konakanchi Venkata Subrahmanya