A COYOTE-INSPIRED APPROACH FOR SYSTEMIC LUPUS ERYTHEMATOSUS PREDICTION USING NEURAL NETWORKS

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DOI

Sobhana Mummaneni

sobhana@vrsiddhartha.ac.in

https://orcid.org/0000-0001-5938-5740
Pragathi Dodda

pragathidodda@gmail.com

Naga Deepika Ginjupalli

deepikaginjupalli3@gmail.com

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 erythematosus

References

Article Details

Mummaneni, S., Dodda, P., & Ginjupalli, N. D. (2024). A COYOTE-INSPIRED APPROACH FOR SYSTEMIC LUPUS ERYTHEMATOSUS PREDICTION USING NEURAL NETWORKS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(2), 22–27. https://doi.org/10.35784/iapgos.6077