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.com
Velagapudi 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 erythematosus

Abbasifard M. et al.: Effects of N-acetylcysteine on systemic lupus erythematosus disease activity and its associated complications: a randomized double-blind clinical trial study. Trials 24(1), 2023, 1–7.
  Google Scholar

Alazwari S. et al.: Improved Coyote Optimization Algorithm and Deep Learning Driven Activity Recognition in Healthcare. IEEE Access, 2024.
  Google Scholar

Ali E. S. et al.: Implementation of coyote optimization algorithm for solving unit commitment problem in power systems. Energy 263, 2023, 125697.
  Google Scholar

Barbhaiya M. et al.: Association of Ultraviolet B Radiation and Risk of Systemic Lupus Erythematosus Among Women in the Nurses’ Health Studies. Arthritis Care & Research, 2023.
  Google Scholar

Basawaraj B. G., Channappa B.: Hybrid coyote predator with DL network for brain disorder detection using EEG signals. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023, 1–15.
  Google Scholar

Ceccarelli F. et al.: Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models. PloS one 12(3), 2017, e0174200 [http://doi.org/10.1371/journal.pone.0174200].
  Google Scholar

Choi M. Y. et al.: Association of Sleep Deprivation and the Risk of Developing Systemic Lupus Erythematosus Among Women. Arthritis Care & Research 75(6), 2023, 1206–1212.
  Google Scholar

Cui J. et al.: Risk prediction models for incident systemic lupus erythematosus among women in the Nurses’ health study cohorts using genetics, family history, and lifestyle and environmental factors. Seminars in Arthritis and Rheumatism 58, 2023, WB Saunders.
  Google Scholar

De Souza R. C. T. et al.: Binary coyote optimization algorithm for feature selection. Pattern Recognition 107, 2020, 107470.
  Google Scholar

Diab A. et al.: Coyote optimization algorithm for parameters estimation of various models of solar cells and PV modules. IEEE Access 8, 2020, 111102–111140.
  Google Scholar

García E. G. et al.: The impact of disease activity on health-related quality of life in patients with systemic lupus erythematosus. Medicina Clínica (English Edition) 160(10), 2023, 428–433.
  Google Scholar

Jiang Z. et al.: Identification of diagnostic biomarkers in systemic lupus erythematosus based on bioinformatics analysis and machine learning. Frontiers in Genetics 13, 2022, 865559.
  Google Scholar

Kim J. W. et al.: Sex hormones affect the pathogenesis and clinical characteristics of systemic lupus erythematosus. Frontiers in Medicine 9, 2022, 906475.
  Google Scholar

Kumar A. et al.: IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier. Biomedical Signal Processing and Control 76, 2022, 103638.
  Google Scholar

Lazar S., Kahlenberg J. M.: Systemic lupus erythematosus: new diagnostic and therapeutic approaches. Annual review of medicine 74, 2023, 339–352.
  Google Scholar

Li L. et al.: Fuzzy multilevel image thresholding based on improved coyote optimization algorithm. IEEE Access 9, 2021, 33595–33607.
  Google Scholar

Masood F. et al.: Novel approach to evaluate classification algorithms and feature selection filter algorithms using medical data. Journal of Computational and Cognitive Engineering 2(1), 2023, 57–67.
  Google Scholar

Parodis I. et al.: EULAR recommendations for the non-pharmacological management of systemic lupus erythematosus and systemic sclerosis. Annals of the Rheumatic Diseases, 2023.
  Google Scholar

Parthiban K., Kamarasan M.: Diabetic retinopathy detection and grading of retinal fundus images using coyote optimization algorithm with deep learning. Multimedia Tools and Applications 82(12), 2023, 18947–18966.
  Google Scholar

Petri M. et al.: Effect of systemic lupus erythematosus and immunosuppressive agents on COVID‐19 vaccination antibody response. Arthritis Care & Research, 2023.
  Google Scholar

Reddy S. et al.: CoySvM-(GeD): Coyote optimization-based support vector machine classifier for cancer classification using gene expression data. Journal of Sensors, 2022, 1–9.
  Google Scholar

Ribeiro M. et al.: Dengue Cases Forecasting Based on eXtreme Gradient Boosting Ensemble with Coyote Optimization. Training 92(7), 2006, 128–148.
  Google Scholar

Seetha J. et al.: Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model. Multimedia Tools and Applications, 2023, 1–27.
  Google Scholar

Singh N et al.: Birth Outcomes and Rehospitalizations Among Pregnant Women With Rheumatoid Arthritis and Systemic Lupus Erythematosus and Their Offspring. Arthritis Care & Research, 2023.
  Google Scholar

Sobhana M. et al.: Hybrid Deep Learning Model for Prediction of Systemic Lupus Erythematosus. International Journal of Intelligent Systems and Applications in Engineering 11(4), 2023, 583–590.
  Google Scholar

Tong H. et al.: Chaotic coyote optimization algorithm for image encryption and steganography. Multimedia Tools and Applications, 2023, 1–27.
  Google Scholar

Wang D. C. et al.: Systemic lupus erythematosus with high disease activity identification based on machine learning. Inflammation Research 72(9), 2023, 1909–1918.
  Google Scholar

Download


Published
2024-06-30

Cited by

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

Authors

Sobhana Mummaneni 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0000-0001-5938-5740

Authors

Pragathi Dodda 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India

Authors

Naga Deepika Ginjupalli 
deepikaginjupalli3@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India

Statistics

Abstract views: 110
PDF downloads: 78


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Most read articles by the same author(s)