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Wang Y, Xia Y, Ling D, Sun J, Wang Y. A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma. Proc Inst Mech Eng H 2023; 237:1409-1426. [PMID: 37877733 DOI: 10.1177/09544119231205664] [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] [Indexed: 10/26/2023]
Abstract
Esophageal squamous cell carcinoma (ESCC) is a type of cancer and has some of the highest rates of both incidence and mortality globally. Developing accurate models for survival prediction provides a basis clinical judgment and decision making, improving the survival status of ESCC patients. Although many predictive models have been developed, there is still lack of highly accurate survival prediction models for ESCC patients. This study proposes a novel survival prediction model for ESCC patients based on principal component analysis (PCA) and least-squares support vector machine (LSSVM) optimized by an improved dragonfly algorithm with hybrid strategy (HSIDA). The original 17 blood indicators are condensed into five new variables by PCA, reducing data dimensionality and redundancy. An improved dragonfly algorithm based on hybrid strategy is proposed, which addresses the limitations of dragonfly algorithm, such as slow convergence, low search accuracy and insufficient vitality of late search. The proposed HSIDA is used to optimize the regularization parameter and kernel parameter of LSSVM, improving the prediction accuracy of the model. The proposed model is validated on the dataset of 400 patients with ESCC in the clinical database of First Affiliated Hospital of Zhengzhou University and the State Key Laboratory of Esophageal Cancer Prevention and Control of Henan Province. The experiment results demonstrate that the proposed HSIDA-LSSVM has the best prediction performance than LSSVM, HSIDA-BP, IPSO-LSSVM, COA-LSSVM and IBA-LSSVM. The proposed model achieves the accuracy of 96.25%, sensitivity of 95.12%, specificity of 97.44%, precision of 97.50%, and F1 score of 96.30%.
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Affiliation(s)
| | | | | | | | - Yan Wang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
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Ishfaq M, Shah SZA, Ahmad I, Rahman Z. Multinomial classification of NLRP3 inhibitory compounds based on large scale machine learning approaches. Mol Divers 2023:10.1007/s11030-023-10690-y. [PMID: 37418166 DOI: 10.1007/s11030-023-10690-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
The role of NLRP3 inflammasome in innate immunity is newly recognized. The NLRP3 protein is a family of nucleotide-binding and oligomerization domain-like receptors as well as a pyrin domain-containing protein. It has been shown that NLRP3 may contribute to the development and progression of a variety of diseases, such as multiple sclerosis, metabolic disorders, inflammatory bowel disease, and other auto-immune and auto-inflammatory conditions. The use of machine learning methods in pharmaceutical research has been widespread for several decades. An important objective of this study is to apply machine learning approaches for the multinomial classification of NLRP3 inhibitors. However, data imbalances can affect machine learning. Therefore, a synthetic minority oversampling technique (SMOTE) has been developed to increase the sensitivity of classifiers to minority groups. The QSAR modelling was performed using 154 molecules retrieved from the ChEMBL database (version 29). The accuracy of the multiclass classification top six models was found to fall within ranges of 0.99 to 0.86, and log loss ranges of 0.2 to 2.3, respectively. The results showed that the receiver operating characteristic curve (ROC) plot values significantly improved when tuning parameters were adjusted and imbalanced data was handled. Moreover, the results demonstrated that SMOTE offers a significant advantage in handling imbalanced datasets as well as substantial improvements in overall accuracy of machine learning models. The top models were then used to predict data from unseen datasets. In summary, these QSAR classification models exhibited robust statistical results and were interpretable, which strongly supported their use for rapid screening of NLRP3 inhibitors.
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Affiliation(s)
- Muhammad Ishfaq
- College of Computer Science, Huanggang Normal University, Huanggang, 438000, China
| | - Syed Zahid Ali Shah
- Department of Pathology, Faculty of Veterinary and Animal Sciences, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Ijaz Ahmad
- The University of Agriculture Peshawar, Peshawar, 25130, Khyber Pakhtunkhwa, Pakistan
| | - Ziaur Rahman
- College of Computer Science, Huanggang Normal University, Huanggang, 438000, China.
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Yan Y, Qian Y, Ma H, Hu C. Research on imbalanced data fault diagnosis of on-load tap changers based on IGWO-WELM. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:4877-4895. [PMID: 36896527 DOI: 10.3934/mbe.2023226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least 5% compared with existing methods.
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Affiliation(s)
- Yan Yan
- State Grid Ningxia Electric Power Research Institute, Yinchuan 750002, China
- College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
| | - Yong Qian
- State Grid Ningxia Electric Power Research Institute, Yinchuan 750002, China
| | - Hongzhong Ma
- College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
| | - Changwu Hu
- Zhongwei Power Supply Company of State Grid Ningxia Electric Power Co. Ltd, Zhongwei 755000, China
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Anil Kumar C, Harish S, Ravi P, SVN M, Kumar BPP, Mohanavel V, Alyami NM, Priya SS, Asfaw AK. Lung Cancer Prediction from Text Datasets Using Machine Learning. BIOMED RESEARCH INTERNATIONAL 2022; 2022:6254177. [PMID: 35872862 PMCID: PMC9303121 DOI: 10.1155/2022/6254177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/10/2022] [Accepted: 06/20/2022] [Indexed: 11/18/2022]
Abstract
Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.
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Affiliation(s)
- C. Anil Kumar
- Department of Electronics and Communication Engineering, R. L. Jalappa Institute of Technology Doddaballapur, Bangalore, Karnataka 561203, India
| | - S. Harish
- Department of Electronics and Communication Engineering, R. L. Jalappa Institute of Technology Doddaballapur, Bangalore, Karnataka 561203, India
| | - Prabha Ravi
- Medical Electronics Engineering, Ramaiah Institute of Technology, Bangalore, Karnataka 560054, India
| | - Murthy SVN
- Department of Computer Science and Engineering, S J C Institute of Technology, Chikkaballapur, Karnataka 562101, India
| | - B. P. Pradeep Kumar
- Department of Electronics and Communication Engineering, HKBK College of Engineering, Bangalore, Karnataka 560045, India
| | - V. Mohanavel
- Centre for Materials Engineering and Regenerative Medicine, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India
- Department of Mechanical Engineering, Chandigarh University, Mohali, 140413 Punjab, India
| | - Nouf M. Alyami
- Department of Zoology, College of Science, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia
| | - S. Shanmuga Priya
- Department of Microbiology-Immunology, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Amare Kebede Asfaw
- Department of Computer Science, Kombolcha Institute of Technology, Wollo University, Ethiopia
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