A COYOTE-INSPIRED APPROACH FOR SYSTEMIC LUPUS ERYTHEMATOSUS PREDICTION USING NEURAL NETWORKS
Sobhana Mummaneni
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0000-0001-5938-5740
Pragathi Dodda
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
Naga Deepika Ginjupalli
deepikaginjupalli3@gmail.comVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
Abstract
Systemic Lupus Erythematosus (SLE) is a complicated autoimmune disease that can present with a variety of clinical symptoms, making precise prognosis difficult. Because SLE has a wide range of symptoms and may overlap with other autoimmune and inflammatory disorders, making a diagnosis can be challenging. This study creates a precise and accurate model for the prediction of SLE using the GEO dataset. For cost-effective data collection and analysis, feature selection might be essential in some applications, particularly in healthcare and scientific research. The strength of Artificial Neural Networks (ANN) for Systemic Lupus Erythematosus prediction and the Coyote Optimization Algorithm (COA) for feature selection are combined in this study. The COA is an optimization method influenced by nature and coyote hunting behavior. This study attempts to improve the effectiveness of subsequent predictive modeling by using COA to identify a subset of significant features from high-dimensional datasets linked to SLE. A Multi-layer Feed-forward Neural Network, a potent machine learning architecture renowned for its capacity to discover complex patterns and correlations within data, is then given the chosen features. Because the neural network is built to capture SLE's intricate and non-linear structure, it offers a reliable foundation for precise classification and prediction. The accuracy of the COA-ANN model was 99.6%.
Keywords:
neural networks, coyote optimization algorithm, computer prediction, systemic lupus erythematosusReferences
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Authors
Sobhana MummaneniVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0000-0001-5938-5740
Authors
Pragathi DoddaVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
Authors
Naga Deepika Ginjupallideepikaginjupalli3@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
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