Improved versions of snake optimizer for feature selection in medical diagnosis: a real case COVID-19

Braik, Malik; Hammouri, Abdelaziz I.; Awadallah, Mohammed A.; Al-Betar, Mohammed Azmi; Alzubi, Omar A.

doi:10.1007/s00500-023-09062-3

Cited by 6 publications

(2 citation statements)

References 83 publications

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“…The recent pandemic has forced researchers to use natureinspired metaheuristic optimization algorithms to target complex datasets. Among these, algorithms such as the capuchin search algorithm (Braik et al, 2023a), particle swarm optimization sine cosine algorithm (Somgiat and Chansamorn, 2022), whale optimizer (Nadimi-Shahraki et al, 2022), and snake optimizer (Braik et al, 2023b) have shown promising results in ML and DL applications. However, our study uses PSO optimization due to its robustness in determining complex, multidimensional search spaces from clinical data and its convergence toward optimal solutions (Kennedy and Eberhart, 1995).…”

Section: Related Workmentioning

confidence: 99%

PSO-XnB: a proposed model for predicting hospital stay of CAD patients

Miriyala,

Sinha

2024

Front. Artif. Intell.

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Coronary artery disease poses a significant challenge in decision-making when predicting the length of stay for a hospitalized patient. This study presents a predictive model—a Particle Swarm Optimized-Enhanced NeuroBoost—that combines the deep autoencoder with an eXtreme gradient boosting model optimized using particle swarm optimization. The model uses a fuzzy set of rules to categorize the length of stay into four distinct classes, followed by data preparation and preprocessing. In this study, the dimensionality of the data is reduced using deep neural autoencoders. The reconstructed data obtained from autoencoders is given as input to an eXtreme gradient boosting model. Finally, the model is tuned with particle swarm optimization to obtain optimal hyperparameters. With the proposed technique, the model achieved superior performance with an overall accuracy of 98.8% compared to traditional ensemble models and past research works. The model also scored highest in other metrics such as precision, recall, and particularly F1 scores for all categories of hospital stay. These scores validate the suitability of our proposed model in medical healthcare applications.

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Section: Related Workmentioning

confidence: 99%

PSO-XnB: a proposed model for predicting hospital stay of CAD patients

Miriyala,

Sinha

2024

Front. Artif. Intell.

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“…Researchers have worked with the Bayesian Belief Network due to its nature and suitability in the clinical world (Kharya et al, 2022 ). In the clinical sector, the Bayesian network projects conditional dependencies and independencies between the various symptoms of illness (Fazel Zarandi et al, 2018 ; Braik et al, 2023a ). For example, “physical inactivity” may cause “obesity,” which may raise “blood glucose.” Moreover, BBN plays a significant role in understanding the association between symptoms.…”

Section: Introductionmentioning

confidence: 99%

Weighted Bayesian Belief Network for diabetics: a predictive model

Kharya,

Soni,

Pati

et al. 2024

Front. Artif. Intell.

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Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the “diabetes capital of the world” owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation. Owing to the concealed early symptoms, numerous diabetic patients go undiagnosed, leading to delayed treatment. While Computational Intelligence approaches have been utilized to improve the prediction rate, a significant portion of these methods lacks interpretability, primarily due to their inherent black box nature. Rule extraction is frequently utilized to elucidate the opaque nature inherent in machine learning algorithms. Moreover, to resolve the black box nature, a method for extracting strong rules based on Weighted Bayesian Association Rule Mining is used so that the extracted rules to diagnose any disease such as diabetes can be very transparent and easily analyzed by the clinical experts, enhancing the interpretability. The WBBN model is constructed utilizing the UCI machine learning repository, demonstrating a performance accuracy of 95.8%.

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