A New Evolutionary Machine Learning Approach for Identifying Pyrene Induced Hepatotoxicity and Renal Dysfunction in Rats

Zhu, Jiayin; Zhu, Fengting; Huang, Siyi; Wang, Gang; Chen, Huiling; Zhao, Xuehua; Zhang, Shuyun

doi:10.1109/access.2018.2889151

Cited by 10 publications

(5 citation statements)

References 59 publications

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“…In this section, we need to run a set of experiments to substantiate the HHO-based model's efficacy in diagnosing the COVID-19. The investigated methods, comprising HHO-FKNN and GWO-FKNN [112], were realized from scratch based on the software of MATLAB. We normalized the input data to be inside [−1, 1] in advance of the classification's performance.…”

Section: B Experimental Setupmentioning

confidence: 99%

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Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

Zhu

et al. 2021

IEEE Access

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This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction. INDEX TERMSCOVID-19, coronavirus, fuzzy K-nearest neighbor, Harris hawk optimization, disease diagnosis, feature selection. I. INTRODUCTION Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease, and the World Health Organization (WHO) declared that the COVID-19 was an international public health emergency [1], [2]. First described COVID-19 in December 2019 in Wuhan, Hubei Province, China. The ongoing outbreak of COVID-19 is affecting multiple countries in the world [1]. Until Mar 11th, 2020, The associate editor coordinating the review of this manuscript and approving it for publication was Juan Wang . 118,326 cases of COVID-19 were diagnosed worldwide, including 80,955 cases in China and 37,371 cases outside China. Additionally, 4,292 deaths have been triggered by COVID-19 [3]. Many countries are facing increased pressures on health care resources. Up to now, a great deal of studies is focused on using traditional statistical methods to identify risk factors of COVID-19 patients. As an example, older age, pre-existing diseases, abnormal liver function, and T-lymphocyte count were correlated closely with COVID-19 progression and prognosis [4]-[6]. However, traditional statistical methods could not rapidly identify changes

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Section: B Experimental Setupmentioning

confidence: 99%

“…The involved methods, HHO-FKNN and GWO-FKNN [112] were both implemented from scratch in the MAT- LAB environment. We have respected fair comparisons referring to the neural network literature [113]- [115].…”

Section: B Experimental Setupmentioning

confidence: 99%

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

Zhu

et al. 2021

IEEE Access

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“…We previously discovered that certain blood screening indicators caused pyrene-induced hepatotoxicity in rats with an overall accuracy of 94.62%, 91.71% of sensitivity and a specificity of 98.33%. 5 On the other hand, pyrene's negative effects have not received enough attention in past studies. Jongeneelen and ten Berge 6 established a physiology-derived toxicokinetic model for predicting humans' pyrene/pyrene-metabolite urine excretion.…”

Section: Introductionmentioning

confidence: 99%

“…Even though studies on pyrene have been documented, few publications have found and validated rodent pyrene exposure. We previously discovered that certain blood screening indicators caused pyrene‐induced hepatotoxicity in rats with an overall accuracy of 94.62%, 91.71% of sensitivity and a specificity of 98.33% 5 . On the other hand, pyrene's negative effects have not received enough attention in past studies.…”

Section: Introductionmentioning

confidence: 99%

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Su,

Zhao,

Heidari

et al. 2023

Basic Clin Pharma Tox

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Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover, and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2, and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.

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“…Since the GWO has fewer parameters, on the contrary, this strategy is relatively simple, flexible, and scalable, and the algorithm has a good convergence effect on the unimodal function. At present, this method has been applied in many fields, including neural network [15,16,63,64], environment [65], medical diagnosis [17,[66][67][68], and image processing [69]. In this research, the GWO algorithm is introduced into CLPSO to generate a novel algorithm called GCLPSO, which can reach a certain harmony between local search and global search to enhance the ability of the algorithm when finding the optimum.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme

Chen

Wang

et al. 2020

Complexity

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In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems in engineering and data science. When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. However, when the single modal function is processed, the convergence speed of the algorithm is too slow to converge quickly to the optimum during optimization. In this paper, the elite-based dominance scheme of another well-established method, grey wolf optimizer (GWO), is introduced into the CLPSO, and the grey wolf local enhanced comprehensive learning PSO algorithm (GCLPSO) is proposed. Thanks to the exploitative trends of the GWO, the algorithm improves the local search capacity of the CLPSO. The new variant is compared with 15 representative and advanced algorithms on IEEE CEC2017 benchmarks. Experimental outcomes have shown that the improved algorithm outperforms other comparison competitors when coping with four different kinds of functions. Moreover, the algorithm is favorably utilized in feature selection and three constrained engineering construction problems. Simulations have shown that the GCLPSO is capable of effectively dealing with constrained problems and solves the problems encountered in actual production.

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A New Evolutionary Machine Learning Approach for Identifying Pyrene Induced Hepatotoxicity and Renal Dysfunction in Rats

Cited by 10 publications

References 59 publications

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme

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