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Lin J, Zhu F, Dong X, Li R, Liu J, Xia J. Enhancing gastric cancer early detection: A multi-verse optimized feature selection model with crossover-information feedback. Comput Biol Med 2024; 175:108535. [PMID: 38714049 DOI: 10.1016/j.compbiomed.2024.108535] [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: 01/30/2024] [Revised: 04/05/2024] [Accepted: 04/28/2024] [Indexed: 05/09/2024]
Abstract
Gastric cancer (GC), an acknowledged malignant neoplasm, threatens life and digestive system functionality if not detected and addressed promptly in its nascent stages. The indispensability of early detection for GC to augment treatment efficacy and survival prospects forms the crux of this investigation. Our study introduces an innovative wrapper-based feature selection methodology, referred to as bCIFMVO-FKNN-FS, which integrates a crossover-information feedback multi-verse optimizer (CIFMVO) with the fuzzy k-nearest neighbors (FKNN) classifier. The primary goal of this initiative is to develop an advanced screening model designed to accelerate the identification of patients with early-stage GC. Initially, the capability of CIFMVO is validated through its application to the IEEE CEC benchmark functions, during which its optimization efficiency is measured against eleven cutting-edge algorithms across various dimensionalities-10, 30, 50, and 100. Subsequent application of the bCIFMVO-FKNN-FS model to the clinical data of 1632 individuals from Wenzhou Central Hospital-diagnosed with either early-stage GC or chronic gastritis-demonstrates the model's formidable predictive accuracy (83.395%) and sensitivity (87.538%). Concurrently, this investigation delineates age, gender, serum gastrin-17, serum pepsinogen I, and the serum pepsinogen I to serum pepsinogen II ratio as parameters significantly associated with early-stage GC. These insights not only validate the efficacy of our proposed model in the early screening of GC but also contribute substantively to the corpus of knowledge facilitating early diagnosis.
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Affiliation(s)
- Jiejun Lin
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Fangchao Zhu
- Department of Gastroenterology, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Xiaoyu Dong
- Department of Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Rizeng Li
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Jisheng Liu
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
| | - Jianfu Xia
- Department of General Surgery, The Dingli Clinical College of Wenzhou Medical University (Wenzhou Central Hospital), Wenzhou, Zhejiang, 325000, China.
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Yang J, Zhang Y, Jin T, Lei Z, Todo Y, Gao S. Maximum Lyapunov exponent-based multiple chaotic slime mold algorithm for real-world optimization. Sci Rep 2023; 13:12744. [PMID: 37550464 PMCID: PMC10406909 DOI: 10.1038/s41598-023-40080-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/04/2023] [Indexed: 08/09/2023] Open
Abstract
Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers from issues of missing the optimal solution or collapsing to local optimum when facing complicated problems. To conquer these drawbacks, we consider adding a novel multi-chaotic local operator to the bio-shock feedback mechanism of SMA to compensate for the lack of exploration of the local solution space with the help of the perturbation nature of the chaotic operator. Based on this, we propose an improved algorithm, namely MCSMA, by investigating how to improve the probabilistic selection of chaotic operators based on the maximum Lyapunov exponent (MLE), an inherent property of chaotic maps. We implement the comparison between MCSMA with other state-of-the-art methods on IEEE Congress on Evolution Computation (CEC) i.e., CEC2017 benchmark test suits and CEC2011 practical problems to demonstrate its potency and perform dendritic neuron model training to test the robustness of MCSMA on classification problems. Finally, the parameters' sensitivities of MCSMA, the utilization of the solution space, and the effectiveness of the MLE are adequately discussed.
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Affiliation(s)
- Jiaru Yang
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Yu Zhang
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Ting Jin
- School of Science, Nanjing Forestry University, Nanjing, 210037, China
| | - Zhenyu Lei
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Yuki Todo
- Faculty of Electrical, Information and Communication Engineering, Kanazawa University, Ishikawa, 9201192, Japan
| | - Shangce Gao
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan.
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Gul HH, Egrioglu E, Bas E. Statistical learning algorithms for dendritic neuron model artificial neural network based on sine cosine algorithm. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Yu H, Shi J, Qian J, Wang S, Li S. Single dendritic neural classification with an effective spherical search-based whale learning algorithm. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7594-7632. [PMID: 37161164 DOI: 10.3934/mbe.2023328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
McCulloch-Pitts neuron-based neural networks have been the mainstream deep learning methods, achieving breakthrough in various real-world applications. However, McCulloch-Pitts neuron is also under longtime criticism of being overly simplistic. To alleviate this issue, the dendritic neuron model (DNM), which employs non-linear information processing capabilities of dendrites, has been widely used for prediction and classification tasks. In this study, we innovatively propose a hybrid approach to co-evolve DNM in contrast to back propagation (BP) techniques, which are sensitive to initial circumstances and readily fall into local minima. The whale optimization algorithm is improved by spherical search learning to perform co-evolution through dynamic hybridizing. Eleven classification datasets were selected from the well-known UCI Machine Learning Repository. Its efficiency in our model was verified by statistical analysis of convergence speed and Wilcoxon sign-rank tests, with receiver operating characteristic curves and the calculation of area under the curve. In terms of classification accuracy, the proposed co-evolution method beats 10 existing cutting-edge non-BP methods and BP, suggesting that well-learned DNMs are computationally significantly more potent than conventional McCulloch-Pitts types and can be employed as the building blocks for the next-generation deep learning methods.
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Affiliation(s)
- Hang Yu
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Jiarui Shi
- Department of Engineering, Wesoft Company Ltd., Kawasaki-shi 210-0024, Japan
| | - Jin Qian
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Shi Wang
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
| | - Sheng Li
- College of Computer Science and Technology, Taizhou University, Taizhou 225300, China
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Neural Logic Circuits: An evolutionary neural architecture that can learn and generalize. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Multiple individual guided differential evolution with time varying and feedback information-based control parameters. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Winsorized dendritic neuron model artificial neural network and a robust training algorithm with Tukey’s biweight loss function based on particle swarm optimization. GRANULAR COMPUTING 2022. [DOI: 10.1007/s41066-022-00345-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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Yang H, Yu Y, Cheng J, Lei Z, Cai Z, Zhang Z, Gao S. An intelligent metaphor-free spatial information sampling algorithm for balancing exploitation and exploration. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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An Improved Equilibrium Optimizer with a Decreasing Equilibrium Pool. Symmetry (Basel) 2022. [DOI: 10.3390/sym14061227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Big Data is impacting and changing the way we live, and its core lies in the use of machine learning to extract valuable information from huge amounts of data. Optimization problems are a common problem in many steps of machine learning. In the face of complex optimization problems, evolutionary computation has shown advantages over traditional methods. Therefore, many researchers are working on improving the performance of algorithms for solving various optimization problems in machine learning. The equilibrium optimizer (EO) is a member of evolutionary computation and is inspired by the mass balance model in environmental engineering. Using particles and their concentrations as search agents, it simulates the process of finding equilibrium states for optimization. In this paper, we propose an improved equilibrium optimizer (IEO) based on a decreasing equilibrium pool. IEO provides more sources of information for particle updates and maintains a higher population diversity. It can discard some exploration in later stages to enhance exploitation, thus achieving a better search balance. The performance of IEO is verified using 29 benchmark functions from IEEE CEC2017, a dynamic economic dispatch problem, a spacecraft trajectory optimization problem, and an artificial neural network model training problem. In addition, the changes in population diversity and computational complexity brought by the proposed method are analyzed.
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