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All Journal International Journal of Public Health Science (IJPHS)
Rao, Veeranki Venkata Rama Maheswara
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Health Professions

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Evaluation of cardiovascular disease in diabetic patients using machine learning techniques Nrusimhadri, Silpa; Swain, Sangram Keshari; Rao, Veeranki Venkata Rama Maheswara; Reddy, Shiva Shankar; Gadiraju, Mahesh
International Journal of Public Health Science (IJPHS) Vol 13, No 3: September 2024
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijphs.v13i3.24213

Abstract

Machine learning (ML) improves operations in many industries, including medicine. It affects the prognosis of several disorders, including heart disease. If predicted, it may provide doctors with new insights and allow them to treat each patient individually. If anticipated, it may provide medical practitioners with valuable information. Our team uses machine learning algorithms to study heart disease risk. This research will compare decision trees, AdaBoost, support vector machines, artificial neural networks (ANN), and customized ANN. The study will include this analysis. The given model will leverage the dataset of general information and medical test results. Our model uses particle swarm optimization (PSO) and k-nearest neighbors (KNN). Algorithm for feature selection. The model reduces dimensionality using evolutionary algorithms and neural networks. We compared the numerous assessment criteria to the current models, our model, and earlier models. Because of this, the suggested model's suitability was rated with the highest accuracy.
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 Gadiraju, Mahesh Mohanty, Bhabodeepika Nrusimhadri, Silpa Pradhan, Samarendra Narayana Reddy, Shiva Shankar Shankar, Reddy Shiva Srirama Murthy, Konakanchi Venkata Subrahmanya Swain, Sangram Keshari