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Zhao J, Li J, Yao J, Lin G, Chen C, Ye H, He X, Qu S, Chen Y, Wang D, Liang Y, Gao Z, Wu F. Enhanced PSO feature selection with Runge-Kutta and Gaussian sampling for precise gastric cancer recurrence prediction. Comput Biol Med 2024; 175:108437. [PMID: 38669732 DOI: 10.1016/j.compbiomed.2024.108437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/14/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024]
Abstract
Gastric cancer (GC), characterized by its inconspicuous initial symptoms and rapid invasiveness, presents a formidable challenge. Overlooking postoperative intervention opportunities may result in the dissemination of tumors to adjacent areas and distant organs, thereby substantially diminishing prospects for patient survival. Consequently, the prompt recognition and management of GC postoperative recurrence emerge as a matter of paramount urgency to mitigate the deleterious implications of the ailment. This study proposes an enhanced feature selection model, bRSPSO-FKNN, integrating boosted particle swarm optimization (RSPSO) with fuzzy k-nearest neighbor (FKNN), for predicting GC. It incorporates the Runge-Kutta search, for improved model accuracy, and Gaussian sampling, enhancing the search performance and helping to avoid locally optimal solutions. It outperforms the sophisticated variants of particle swarm optimization when evaluated in the CEC 2014 test suite. Furthermore, the bRSPSO-FKNN feature selection model was introduced for GC recurrence prediction analysis, achieving up to 82.082 % and 86.185 % accuracy and specificity, respectively. In summation, this model attains a notable level of precision, poised to ameliorate the early warning system for GC recurrence and, in turn, advance therapeutic options for afflicted patients.
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Affiliation(s)
- Jungang Zhao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - JiaCheng Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Jiangqiao Yao
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Ganglian Lin
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Chao Chen
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Huajun Ye
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Xixi He
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Shanghu Qu
- Department of Urology, Yunnan Tumor Hospital and the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
| | - Yuxin Chen
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Danhong Wang
- Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Yingqi Liang
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Zhihong Gao
- Zhejiang Engineering Research Center of Intelligent Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Fang Wu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Zhang L, Yu R, Chen K, Zhang Y, Li Q, Chen Y. Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model. Comput Biol Med 2024; 173:108294. [PMID: 38537565 DOI: 10.1016/j.compbiomed.2024.108294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/21/2024] [Accepted: 03/12/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes. METHODS This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability. RESULTS Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%. CONCLUSIONS The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches.
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Affiliation(s)
- Lufang Zhang
- The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Renyue Yu
- Cardiac Care Unit, Sir RUN RUN Shaw Hospital, Hangzhou, 310000, China.
| | - Keya Chen
- The First Clinical College, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Ying Zhang
- Wenzhou Medical University School of Nursing, 325000, Wenzhou, 325000, China; Cixi Biomedical Research Institute, Wenzhou Medical University, Cixi, 315300, China.
| | - Qiang Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Yu Chen
- Nursing Department, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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Wei Y, Othman Z, Daud KM, Luo Q, Zhou Y. Advances in Slime Mould Algorithm: A Comprehensive Survey. Biomimetics (Basel) 2024; 9:31. [PMID: 38248605 PMCID: PMC10813181 DOI: 10.3390/biomimetics9010031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 01/23/2024] Open
Abstract
The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.
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Affiliation(s)
- Yuanfei Wei
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Xiangsihu College, Guangxi Minzu University, Nanning 530225, China
| | - Zalinda Othman
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Qifang Luo
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
- Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
| | - Yongquan Zhou
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Xiangsihu College, Guangxi Minzu University, Nanning 530225, China
- College of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China
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Li X, Lin Z, Lv H, Yu L, Heidari AA, Zhang Y, Chen H, Liang G. Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design. iScience 2023; 26:107736. [PMID: 37810256 PMCID: PMC10558746 DOI: 10.1016/j.isci.2023.107736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/21/2023] [Accepted: 08/23/2023] [Indexed: 10/10/2023] Open
Abstract
The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.
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Affiliation(s)
- Xinru Li
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Zihan Lin
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Haoxuan Lv
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Liang Yu
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Ali Asghar Heidari
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Yudong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Huiling Chen
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China
| | - Guoxi Liang
- Department of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, China
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Yu X, Qin W, Lin X, Shan Z, Huang L, Shao Q, Wang L, Chen M. Synergizing the enhanced RIME with fuzzy K-nearest neighbor for diagnose of pulmonary hypertension. Comput Biol Med 2023; 165:107408. [PMID: 37672924 DOI: 10.1016/j.compbiomed.2023.107408] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/19/2023] [Accepted: 08/27/2023] [Indexed: 09/08/2023]
Abstract
Pulmonary hypertension (PH) is an uncommon yet severe condition characterized by sustained elevation of blood pressure in the pulmonary arteries. The delaying treatment can result in disease progression, right ventricular failure, increased risk of complications, and even death. Early recognition and timely treatment are crucial in halting PH progression, improving cardiac function, and reducing complications. Within this study, we present a highly promising hybrid model, known as bERIME_FKNN, which constitutes a feature selection approach integrating the enhanced rime algorithm (ERIME) and fuzzy K-nearest neighbor (FKNN) technique. The ERIME introduces the triangular game search strategy, which augments the algorithm's capacity for global exploration by judiciously electing distinct search agents across the exploratory domain. This approach fosters both competitive rivalry and collaborative synergy among these agents. Moreover, an random follower search strategy is incorporated to bestow a novel trajectory upon the principal search agent, thereby enriching the spectrum of search directions. Initially, ERIME is meticulously compared to 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark functions across diverse dimensionalities such as 10, 30, 50, and 100, ultimately validating its exceptional optimization capability within the model. Subsequently, employing the color moment and grayscale co-occurrence matrix methodologies, a total of 118 features are extracted from 63 PH patients' and 60 healthy individuals' images, alongside an analysis of 14,514 recordings obtained from these patients utilizing the developed bERIME_FKNN model. The outcomes manifest that the bERIME_FKNN model exhibits a conspicuous prowess in the realm of PH classification, attaining an accuracy and specificity exceeding 99%. This implies that the model serves as a valuable computer-aided tool, delivering an advanced warning system for diagnosis and prognosis evaluation of PH.
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Affiliation(s)
- Xiaoming Yu
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Wenxiang Qin
- The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Xiao Lin
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Zhuohan Shan
- The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Liyao Huang
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035, China.
| | - Qike Shao
- Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035, China.
| | - Liangxing Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
| | - Mayun Chen
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
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