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Financial Distress Prediction with a Novel Diversity-Considered GA-MLP Ensemble Algorithm. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10674-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Ramadan SZ. Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:9162464. [PMID: 32300474 PMCID: PMC7091549 DOI: 10.1155/2020/9162464] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 12/25/2019] [Accepted: 02/13/2020] [Indexed: 12/28/2022]
Abstract
According to the American Cancer Society's forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.
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Affiliation(s)
- Saleem Z. Ramadan
- Department of Industrial Engineering, German Jordanian University, Mushaqar 11180, Amman, Jordan
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Attallah O, Karthikesalingam A, Holt PJ, Thompson MM, Sayers R, Bown MJ, Choke EC, Ma X. Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection. Proc Inst Mech Eng H 2017; 231:1048-1063. [PMID: 28925817 DOI: 10.1177/0954411917731592] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.
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Affiliation(s)
- Omneya Attallah
- 1 Department of Electronics and Communications, College of Engineering and Technology, Arab Academy for Science and Technology, Alexandria, Egypt.,2 School of Engineering and Applied Science, Aston University, Birmingham, UK
| | - Alan Karthikesalingam
- 3 St George's Vascular Institute, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Peter Je Holt
- 3 St George's Vascular Institute, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Matthew M Thompson
- 3 St George's Vascular Institute, St George's University Hospitals NHS Foundation Trust, London, UK
| | - Rob Sayers
- 4 NIHR Leicester Cardiovascular Biomedical Research Unit and Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Matthew J Bown
- 4 NIHR Leicester Cardiovascular Biomedical Research Unit and Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Eddie C Choke
- 4 NIHR Leicester Cardiovascular Biomedical Research Unit and Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Xianghong Ma
- 2 School of Engineering and Applied Science, Aston University, Birmingham, UK
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Jamalinia H, Khalouei S, Rezaie V, Nejatian S, Bagheri-Fard K, Parvin H. Diverse classifier ensemble creation based on heuristic dataset modification. J Appl Stat 2017. [DOI: 10.1080/02664763.2017.1363163] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hamid Jamalinia
- Department of Computer Engineering, Islamic Azad University, Nourabad Mamasani, Iran
| | - Saber Khalouei
- Department of Computer Engineering, Islamic Azad University, Yasooj, Iran
| | - Vahideh Rezaie
- Department of Mathematics, Islamic Azad University, Yasooj, Iran
- Young Researchers and Elite Club, Islamic Azad University, Yasooj, Iran
| | - Samad Nejatian
- Young Researchers and Elite Club, Islamic Azad University, Yasooj, Iran
- Department of Electrical Engineering, Islamic Azad University, Yasooj, Iran
| | - Karamolah Bagheri-Fard
- Department of Computer Engineering, Islamic Azad University, Yasooj, Iran
- Young Researchers and Elite Club, Islamic Azad University, Yasooj, Iran
| | - Hamid Parvin
- Department of Computer Engineering, Islamic Azad University, Nourabad Mamasani, Iran
- Young Researchers and Elite Club, Islamic Azad University, Nourabad Mamasani, Iran
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He B, Bai J, Zipunnikov VV, Koster A, Caserotti P, Lange-Maia B, Glynn NW, Harris TB, Crainiceanu CM. Predicting human movement with multiple accelerometers using movelets. Med Sci Sports Exerc 2015; 46:1859-66. [PMID: 25134005 DOI: 10.1249/mss.0000000000000285] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE The study aims were 1) to develop transparent algorithms that use short segments of training data for predicting activity types and 2) to compare the prediction performance of the proposed algorithms using single accelerometers and multiple accelerometers. METHODS Sixteen participants (age, 80.6 yr (4.8 yr); body mass index, 26.1 kg·m (2.5 kg·m)) performed 15 lifestyle activities in the laboratory, each wearing three accelerometers at the right hip and left and right wrists. Triaxial accelerometry data were collected at 80 Hz using ActiGraph GT3X+. Prediction algorithms were developed, which, instead of extracting features, build activity-specific dictionaries composed of short signal segments called movelets. Three alternative approaches were proposed to integrate the information from the multiple accelerometers. RESULTS With at most several seconds of training data per activity, the prediction accuracy at the second-level temporal resolution was very high for lying, standing, normal/fast walking, and standing up from a chair (the median prediction accuracy ranged from 88.2% to 99.9% on the basis of the single-accelerometer movelet approach). For these activities, wrist-worn accelerometers performed almost as well as hip-worn accelerometers (the median difference in accuracy between wrist and hip ranged from -2.7% to 5.8%). Modest improvements in prediction accuracy were achieved by integrating information from multiple accelerometers. DISCUSSION AND CONCLUSIONS It is possible to achieve high prediction accuracy at the second-level temporal resolution with very limited training data. To increase prediction accuracy from the simultaneous use of multiple accelerometers, a careful selection of integrative approaches is required.
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Affiliation(s)
- Bing He
- 1Department of Biostatistics, The Johns Hopkins University, Baltimore, MD; 2Department of Social Medicine, University of Maastricht, Maastricht, THE NETHERLANDS; 3Institute of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, DENMARK; 4Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA; and 5Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD
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Bhatnagar V, Bhardwaj M, Sharma S, Haroon S. Accuracy–diversity based pruning of classifier ensembles. PROGRESS IN ARTIFICIAL INTELLIGENCE 2014. [DOI: 10.1007/s13748-014-0042-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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RE MATTEO, VALENTINI GIORGIO. Ensemble Methods. ADVANCES IN MACHINE LEARNING AND DATA MINING FOR ASTRONOMY 2012. [DOI: 10.1201/b11822-34] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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DE SANTO MASSIMO, PERCANNELLA GENNARO, SANSONE CARLO, VENTO MARIO. A MULTI-EXPERT SYSTEM FOR SHOT CHANGE DETECTION IN MPEG MOVIES. INT J PATTERN RECOGN 2011. [DOI: 10.1142/s0218001404003484] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Shot Change Detection (SCD) in MPEG coded videos is a complex and still open research problem whose interest is growing up more and more due to the diffusion of Video Databases and Digital Libraries. Techniques providing fully satisfactory performances on complex video domains are not yet available even if a number of proposals exist; such proposals show very often to be complementary in their results. In this context, the Authors investigated the use of Multi-Expert Systems (MES) for approaching the SCD problem. In the present paper, we propose and discuss a strategy to select the SCD techniques to be combined and a method for choosing an effective combining rule. In order to assess the performance of the proposed MES, we set up a database that is significantly wider than the ones commonly used in the field. Experimental results demonstrate that the proposed system performs better than each of the single SCD technique considered.
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Affiliation(s)
- MASSIMO DE SANTO
- Dipartimento di Ingegneria dell'Informazione e di Ingegneria Elettrica, Universitá di Salerno, Via P.te Don Melillo, 1, I-84084, Fisciano (SA), Italy
| | - GENNARO PERCANNELLA
- Dipartimento di Ingegneria dell'Informazione e di Ingegneria Elettrica, Universitá di Salerno, Via P.te Don Melillo, 1, I-84084, Fisciano (SA), Italy
| | - CARLO SANSONE
- Dipartimento di Informatica e Sistemistica, Universitá di Napoli "Federico II", Via Claudio, 21 I-80125 Napoli, Italy
| | - MARIO VENTO
- Dipartimento di Ingegneria dell'Informazione e di Ingegneria Elettrica, Universitá di Salerno, Via P.te Don Melillo, 1, I-84084, Fisciano (SA), Italy
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Rasheed S, Stashuk DW, Kamel MS. Diversity-based combination of non-parametric classifiers for EMG signal decomposition. Pattern Anal Appl 2008. [DOI: 10.1007/s10044-008-0103-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Optimized Associative Memories for Feature Selection. PATTERN RECOGNITION AND IMAGE ANALYSIS 2007. [DOI: 10.1007/978-3-540-72847-4_56] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Kuncheva LI. That Elusive Diversity in Classifier Ensembles. PATTERN RECOGNITION AND IMAGE ANALYSIS 2003. [DOI: 10.1007/978-3-540-44871-6_130] [Citation(s) in RCA: 67] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Kuncheva LI, Whitaker CJ. Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse. MULTIPLE CLASSIFIER SYSTEMS 2002. [DOI: 10.1007/3-540-45428-4_8] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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