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Gencer K. Mini Percutaneous Nephrolithotomy vs Standard Percutaneous Nephrolithotomy: A Perioperative Decision Support System for Surgical Success Comparison. Ther Clin Risk Manag 2023; 19:1075-1086. [PMID: 38170095 PMCID: PMC10759910 DOI: 10.2147/tcrm.s444519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024] Open
Abstract
Purpose This study aimed to rank the features that are important in terms of safety and effectiveness in choosing the surgical method and providing appropriate care to the patient by using the variables examined before and after the surgery to evaluate the success of mini percutaneous nephrolithotomy and standard percutaneous nephrolithotomy surgeries. Patients and Methods The features evaluated before and after surgery were ranked according to their importance in the features considered, using Multivariate Adaptive Regression Splines (MARS), LASSO, Ridge, Elastic_net, and Random Forest algorithms as variable selection techniques. There are 278 samples in the relevant data set. Results Type of surgery (100%), intercostal access (97.75%), kidney opening procedure (94.25%), postoperative creatinine (59.22%), hydronephrosis (52.23%), the number of entries (41.61%), and pre- and post-operative hemoglobin difference (45.13%) were determined as the most critical variables. The MARS algorithm showed the most successful performance, with the lowest mean absolute error (MAE) value of 0.3622, the lowest root mean square error (RMSE) value of 0.3960, and the highest R2 value of 0.3405. Conclusion Clinical decision support systems can be helpful in eliminating errors and reducing costs. It can also improve the quality of healthcare and aid in the early diagnosis of diseases. Computer-aided decision-making systems can be developed using the results of such products. These systems can provide doctors with better information about their patient's treatment options and improve decision-making. It can contribute to patients being better informed about the surgery results and taking an active role. In conclusion, this study provides essential information that should be included in the surgical decision-making process for patients using medications and with a history of percutaneous nephrolithotomy.
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Affiliation(s)
- Kerem Gencer
- Afyonkarahisar Health Sciences University, Department of Distance Education Application and Research Center, Afyonkarahisar, Turkey
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Yang Y, Zhao J. Which part of a picture is worth a thousand words: A joint framework for finding and visualizing critical linear features from images. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2023.103370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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Tahmouresi A, Rashedi E, Yaghoobi MM, Rezaei M. Gene selection using pyramid gravitational search algorithm. PLoS One 2022; 17:e0265351. [PMID: 35290401 PMCID: PMC8923457 DOI: 10.1371/journal.pone.0265351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 02/28/2022] [Indexed: 11/24/2022] Open
Abstract
Genetics play a prominent role in the development and progression of malignant neoplasms. Identification of the relevant genes is a high-dimensional data processing problem. Pyramid gravitational search algorithm (PGSA), a hybrid method in which the number of genes is cyclically reduced is proposed to conquer the curse of dimensionality. PGSA consists of two elements, a filter and a wrapper method (inspired by the gravitational search algorithm) which iterates through cycles. The genes selected in each cycle are passed on to the subsequent cycles to further reduce the dimension. PGSA tries to maximize the classification accuracy using the most informative genes while reducing the number of genes. Results are reported on a multi-class microarray gene expression dataset for breast cancer. Several feature selection algorithms have been implemented to have a fair comparison. The PGSA ranked first in terms of accuracy (84.5%) with 73 genes. To check if the selected genes are meaningful in terms of patient’s survival and response to therapy, protein-protein interaction network analysis has been applied on the genes. An interesting pattern was emerged when examining the genetic network. HSP90AA1, PTK2 and SRC genes were amongst the top-rated bottleneck genes, and DNA damage, cell adhesion and migration pathways are highly enriched in the network.
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Affiliation(s)
| | - Esmat Rashedi
- Department of Electrical and Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran
- * E-mail:
| | - Mohammad Mehdi Yaghoobi
- Department of Biotechnology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran
| | - Masoud Rezaei
- Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran
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Dhingra S, Bansal P. Designing of a rigorous image retrieval system with amalgamation of artificial intelligent techniques and relevance feedback. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-189776] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Retrieving out the most comparable images from huge databases is the challenging task for image retrieval systems. So, there is a great need of constructing a capable and rigorous image retrieval system. In this implementation, an exclusive and competent Content based image retrieval (CBIR) system is schemed by the integration of Color moment (CM) and Local binary pattern (LBP). A hybrid feature vector is created by the combination of these two techniques through the process of normalization. This hybrid feature vector is given as the input to the intelligent classifiers i.e. Support vector machine (SVM) and Cascade forward back propagation neural network (CFBPNN). After that, Relevance feedback (RF) technique is applied so as to get the high level information in order to reduce the semantic gap. So, here two Artificial Intelligent CBIR models are proposed, first one is (Hybrid+SVM+RF) and second is (Hybrid+CFBPNN+RF) and their performance parameters are compared. The implementations are performed on two benchmark dataset Corel-1K and Oxford flower dataset which contains 1000 and 1360 images respectively. Different parameters are figured such as accuracy, precision, average retrieval time, recall etc. The average precision obtained for the first model is 93% with Corel 1K database and 91% with Oxford flower database. And similarly for the second model, it is 97% and 94% respectively which is higher than the first model. This implemented technique is validated on both the datasets and the attained results outperforms with other related s approaches.
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Affiliation(s)
- Shefali Dhingra
- ECE Department, Guru Gobind Singh Indraprastha University, New Delhi, India
| | - Poonam Bansal
- Maharaja Surajmal Institute of Technology, New Delhi, India
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An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases. J Digit Imaging 2020; 33:971-987. [PMID: 32399717 DOI: 10.1007/s10278-020-00338-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
The main problem in content-based image retrieval (CBIR) systems is the semantic gap which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which appear in the patient's lung CT scan play a significant role in the identification of cancerous lung nodules and many other lung diseases. In this paper, we propose a new combination of descriptors for the effective retrieval of these imaging signs. First, we construct a feature database by combining local ternary pattern (LTP), local phase quantization (LPQ), and discrete wavelet transform. Next, joint mutual information (JMI)-based feature selection is deployed to reduce the redundancy and to select an optimal feature set for CISs retrieval. To this end, similarity measurement is performed by combining visual and semantic information in equal proportion to construct a balanced graph and the shortest path is computed for learning contextual similarity to obtain final similarity between each query and database image. The proposed system is evaluated on a publicly available database of lung CT imaging signs (LISS), and results are retrieved based on visual feature similarity comparison and graph-based similarity comparison. The proposed system achieves a mean average precision (MAP) of 60% and 0.48 AUC of precision-recall (P-R) graph using only visual features similarity comparison. These results further improve on graph-based similarity measure with a MAP of 70% and 0.58 AUC which shows the superiority of our proposed scheme.
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Fast Image Index for Database Management Engines. JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH 2020. [DOI: 10.2478/jaiscr-2020-0008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
Large-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.
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Huang Z. A feature selection approach combining neural networks with genetic algorithms. AI COMMUN 2020. [DOI: 10.3233/aic-190626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Zhi Huang
- School of Information Engineering, Mianyang Teachers’ College, Sichuan Province, China. E-mail:
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Tarawneh AS, Celik C, Hassanat AB, Chetverikov D. Detailed investigation of deep features with sparse representation and dimensionality reduction in CBIR: A comparative study. INTELL DATA ANAL 2020. [DOI: 10.3233/ida-184411] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Ahmad S. Tarawneh
- Department of Algorithms and their Applications, Eötvös Loránd University, Budapest, Hungary
| | - Ceyhun Celik
- Department of Computer Engineering, Gazi University, Ankara, Turkey
| | - Ahmad B. Hassanat
- Department of Information Technology, Mutah University, Karak, Jordan
- Computer Science Department, Community College, University of Tabuk, Tabuk, Saudi Arabia
- Industrial Innovation and Robotics Center, University of Tabuk, Tabuk, Saudi Arabia
| | - Dmitry Chetverikov
- Department of Algorithms and their Applications, Eötvös Loránd University, Budapest, Hungary
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Unar S, Wang X, Wang C, Wang Y. A decisive content based image retrieval approach for feature fusion in visual and textual images. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.05.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Aziz MAE, Ewees AA, Hassanien AE. Multi-objective whale optimization algorithm for content-based image retrieval. MULTIMEDIA TOOLS AND APPLICATIONS 2018; 77:26135-26172. [DOI: 10.1007/s11042-018-5840-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 02/11/2018] [Accepted: 02/26/2018] [Indexed: 09/02/2023]
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Javidi MM, Zarisfi Kermani F. Utilizing the advantages of both global and local search strategies for finding a small subset of features in a two-stage method. APPL INTELL 2018. [DOI: 10.1007/s10489-018-1159-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Boon KH, Khalil-Hani M, Malarvili MB. Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 153:171-184. [PMID: 29157449 DOI: 10.1016/j.cmpb.2017.10.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 08/27/2017] [Accepted: 10/10/2017] [Indexed: 06/07/2023]
Abstract
This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity.
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Affiliation(s)
- K H Boon
- Faculty of Electrical Engineering, Universiti Tekonologi Malaysia, Skudai, Johor 81310, Malaysia.
| | - M Khalil-Hani
- Faculty of Electrical Engineering, Universiti Tekonologi Malaysia, Skudai, Johor 81310, Malaysia.
| | - M B Malarvili
- Faculty of Electrical Engineering, Universiti Tekonologi Malaysia, Skudai, Johor 81310, Malaysia.
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Design and Construction of Electronic Nose for Multi-purpose Applications by Sensor Array Arrangement Using IBGSA. J INTELL ROBOT SYST 2017. [DOI: 10.1007/s10846-017-0759-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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16
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Zandevakili H, Rashedi E, Mahani A. Gravitational search algorithm with both attractive and repulsive forces. Soft comput 2017. [DOI: 10.1007/s00500-017-2785-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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17
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Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.11.018] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Boon KH, Khalil-Hani M, Malarvili MB, Sia CW. Paroxysmal atrial fibrillation prediction method with shorter HRV sequences. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 134:187-196. [PMID: 27480743 DOI: 10.1016/j.cmpb.2016.07.016] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 06/12/2016] [Accepted: 07/04/2016] [Indexed: 06/06/2023]
Abstract
This paper proposes a method that predicts the onset of paroxysmal atrial fibrillation (PAF), using heart rate variability (HRV) segments that are shorter than those applied in existing methods, while maintaining good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to stabilize (electrically) and prevent the onset of atrial arrhythmias with different pacing techniques. We investigate the effect of HRV features extracted from different lengths of HRV segments prior to PAF onset with the proposed PAF prediction method. The pre-processing stage of the predictor includes QRS detection, HRV quantification and ectopic beat correction. Time-domain, frequency-domain, non-linear and bispectrum features are then extracted from the quantified HRV. In the feature selection, the HRV feature set and classifier parameters are optimized simultaneously using an optimization procedure based on genetic algorithm (GA). Both full feature set and statistically significant feature subset are optimized by GA respectively. For the statistically significant feature subset, Mann-Whitney U test is used to filter non-statistical significance features that cannot pass the statistical test at 20% significant level. The final stage of our predictor is the classifier that is based on support vector machine (SVM). A 10-fold cross-validation is applied in performance evaluation, and the proposed method achieves 79.3% prediction accuracy using 15-minutes HRV segment. This accuracy is comparable to that achieved by existing methods that use 30-minutes HRV segments, most of which achieves accuracy of around 80%. More importantly, our method significantly outperforms those that applied segments shorter than 30 minutes.
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Affiliation(s)
- K H Boon
- Faculty of Electrical Engineering, Universiti Tekonologi Malaysia, 81310 Skudai, Johor, Malaysia.
| | - M Khalil-Hani
- Faculty of Electrical Engineering, Universiti Tekonologi Malaysia, 81310 Skudai, Johor, Malaysia
| | - M B Malarvili
- Faculty of Electrical Engineering, Universiti Tekonologi Malaysia, 81310 Skudai, Johor, Malaysia
| | - C W Sia
- Faculty of Electrical Engineering, Universiti Tekonologi Malaysia, 81310 Skudai, Johor, Malaysia
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Bhaumik H, Bhattacharyya S, Nath MD, Chakraborty S. Hybrid soft computing approaches to content based video retrieval: A brief review. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.03.022] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Unsupervised margin-based feature selection using linear transformations with neighbor preservation. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.07.089] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2014.06.075] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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