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Review on Machine Learning Techniques for Medical Data Classification and Disease Diagnosis. REGENERATIVE ENGINEERING AND TRANSLATIONAL MEDICINE 2022. [DOI: 10.1007/s40883-022-00273-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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2
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Zhao T, Chen C, Cao H. Evolutionary self-organizing fuzzy system using fuzzy-classification-based social learning particle swarm optimization. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.05.055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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3
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Peng H, Qian J, Kong F, Fan D, Shao P, Wu Z. Enhancing firefly algorithm with sliding window for continuous optimization problems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07193-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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4
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Mazoure B, Mazoure A, Bédard J, Makarenkov V. DUNEScan: a web server for uncertainty estimation in skin cancer detection with deep neural networks. Sci Rep 2022; 12:179. [PMID: 34996997 PMCID: PMC8741961 DOI: 10.1038/s41598-021-03889-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 12/10/2021] [Indexed: 11/16/2022] Open
Abstract
Recent years have seen a steep rise in the number of skin cancer detection applications. While modern advances in deep learning made possible reaching new heights in terms of classification accuracy, no publicly available skin cancer detection software provide confidence estimates for these predictions. We present DUNEScan (Deep Uncertainty Estimation for Skin Cancer), a web server that performs an intuitive in-depth analysis of uncertainty in commonly used skin cancer classification models based on convolutional neural networks (CNNs). DUNEScan allows users to upload a skin lesion image, and quickly compares the mean and the variance estimates provided by a number of new and traditional CNN models. Moreover, our web server uses the Grad-CAM and UMAP algorithms to visualize the classification manifold for the user’s input, hence providing crucial information about its closeness to skin lesion images from the popular ISIC database. DUNEScan is freely available at: https://www.dunescan.org.
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Affiliation(s)
- Bogdan Mazoure
- School of Computer Science, McGill University and Quebec AI Institute (MILA), Montreal, Canada.
| | | | - Jocelyn Bédard
- Département d'Informatique, Université du Québec à Montréal, Montreal, Canada
| | - Vladimir Makarenkov
- Département d'Informatique, Université du Québec à Montréal, Montreal, Canada
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Beheshti Roui M, Zomorodi M, Sarvelayati M, Abdar M, Noori H, Pławiak P, Tadeusiewicz R, Zhou X, Khosravi A, Nahavandi S, Acharya UR. A novel approach based on genetic algorithm to speed up the discovery of classification rules on GPUs. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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6
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Baygin M, Tuncer T, Dogan S, Tan RS, Acharya UR. Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.06.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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7
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Mishra A, Uyyala SR, A VR. A Novel Probabilistic-Based Deep Neural Network: Toward the Selection of Wart Treatment. Cognit Comput 2021. [DOI: 10.1007/s12559-021-09882-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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8
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Tuncer T, Dogan S, Ertam F, Subasi A. A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals. Cogn Neurodyn 2021; 15:223-237. [PMID: 33854641 PMCID: PMC7969686 DOI: 10.1007/s11571-020-09601-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 05/10/2020] [Accepted: 05/14/2020] [Indexed: 12/24/2022] Open
Abstract
Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Fatih Ertam
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Abdulhamit Subasi
- College of Engineering, Department of Computer Science, Effat University, Jeddah, 21478 Saudi Arabia
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Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique. MATHEMATICS 2020. [DOI: 10.3390/math9010017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Bankruptcy prediction has been broadly investigated using financial ratios methodologies. One involved factor is the quality of the portfolio of loans which is given. Hence, having a model to classify/predict position of each loan candidate based on several features is important. In this work, an application of machine learning approach in mathematical finance and banking is discussed. It is shown how we can classify some lending portfolios of banks under several features such as rating categories and various maturities. Dynamic updates of the portfolio are also given along with the top probabilities showing how the financial data of this type can be classified. The discussions and results reveal that a good algorithm for doing such a classification on large economic data of such type is the k-nearest neighbors (KNN) with k=1 along with parallelization even over the support vector machine, random forest, and artificial neural network techniques to save as much as possible on computational time.
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Janković R, Mihajlović I, Štrbac N, Amelio A. Machine learning models for ecological footprint prediction based on energy parameters. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05476-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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11
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A Novel Approach for Coronary Artery Disease Diagnosis using Hybrid Particle Swarm Optimization based Emotional Neural Network. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.09.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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12
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Gholami J, Pourpanah F, Wang X. Feature selection based on improved binary global harmony search for data classification. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106402] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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13
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Basiri ME, Abdar M, Cifci MA, Nemati S, Acharya UR. A novel method for sentiment classification of drug reviews using fusion of deep and machine learning techniques. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.105949] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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14
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The selection of wart treatment method based on Synthetic Minority Over-sampling Technique and Axiomatic Fuzzy Set theory. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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P KA, Acharjya DP. A Hybrid Scheme for Heart Disease Diagnosis Using Rough Set and Cuckoo Search Technique. J Med Syst 2019; 44:27. [PMID: 31828437 DOI: 10.1007/s10916-019-1497-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 11/05/2019] [Indexed: 01/29/2023]
Abstract
Large volumes of raw data are created from the digital world every day. Acquiring useful information from these data is challenging, and it turned into a prime zone of momentum explore. More research is done in this direction. Further, in disease diagnosis, many uncertainties are involved in the information system. To handle such uncertainties, intelligent techniques are employed. In this paper, we present an integrated scheme for heart disease diagnosis. The proposed model integrates cuckoo search and rough set for inferencing decision rules. At the underlying phase, we employ a cuckoo search to discover the main features. Further, these main features are analyzed using rough set generating rules. An empirical analysis is carried out on heart disease. Besides, a comparative study is also presented. The comparative study demonstrates the feasibility of the proposed model.
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Affiliation(s)
- Kauser Ahmed P
- School of Computer Science and Engineering, VIT, Vellore, India
| | - D P Acharjya
- School of Computer Science and Engineering, VIT, Vellore, India.
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Yaman O, Ertam F, Tuncer T. Automated Parkinson's disease recognition based on statistical pooling method using acoustic features. Med Hypotheses 2019; 135:109483. [PMID: 31954340 DOI: 10.1016/j.mehy.2019.109483] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/06/2019] [Accepted: 11/08/2019] [Indexed: 02/08/2023]
Abstract
Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson's disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson's disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson's disease recognition.
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Affiliation(s)
- Orhan Yaman
- Department of Informatics, Firat University, Elazig, Turkey.
| | - Fatih Ertam
- Department of Digital Forensics Engineering, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Firat University, Elazig, Turkey.
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