Atom Taylor Bird Swarm algorithm-based deep belief network for incremental classification using medical data

Medical data classification is considered as a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but to achieve higher performance in terms of classification accuracy result in a great challenge in the medical sector due to the presence of missing values, uncertainty and redundant attributes. Hence, an effective and optimal data classification method named Atom Taylor Bird Swarm Algorithm-based Deep Belief Network (Atom Taylor BSA-based DBN) is proposed in this research to perform incremental classification using medical data. The proposed Atom Taylor BSA is designed by integrating the atom search optimization (ASO) with the Taylor Bird Swarm algorithm (Taylor BSA), which is the development of the Taylor series with BSA. The DBN classifier effectively performs the incremental classification using medical data with its associated neurons and generates optimal result based on the fitness measure. Accordingly, the selected features enable the classifier to increase the performance of classification accuracy. However, the proposed Atom Taylor BSA-based DBN obtained better performance using the metrics, like specificity, sensitivity, and accuracy with the values of 0.8307, 0.9074, and 0.8804, using Hungarian dataset.

[1]  K. Alagarsamy,et al.  Taylor Series Prediction of Time Series Data with Error Propagated by Artificial Neural Network , 2014 .

[2]  Tong Li,et al.  Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification , 2020, Inf. Sci..

[3]  Hossam Faris,et al.  Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection , 2019, Nature-Inspired Optimizers.

[4]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[5]  Federico Cabitza,et al.  Exploring Medical Data Classification with Three-Way Decision Trees , 2019, HEALTHINF.

[6]  Chee Peng Lim,et al.  Feature selection based on brain storm optimization for data classification , 2019, Appl. Soft Comput..

[7]  Suphakant Phimoltares,et al.  Streaming chunk incremental learning for class-wise data stream classification with fast learning speed and low structural complexity , 2019, PloS one.

[8]  Dawid Połap,et al.  Analysis of Skin Marks Through the Use of Intelligent Things , 2019, IEEE Access.

[9]  Chunhua Su,et al.  A physiological and behavioral feature authentication scheme for medical cloud based on fuzzy-rough core vector machine , 2020, Inf. Sci..

[10]  Daguang Xu,et al.  V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation , 2019, 2019 International Conference on 3D Vision (3DV).

[11]  Sambit Bakshi,et al.  A memetic algorithm using emperor penguin and social engineering optimization for medical data classification , 2019, Appl. Soft Comput..

[12]  M. Subramaniam,et al.  Multi level incremental influence measure based classification of medical data for improved classification , 2018, Cluster Computing.

[13]  S. Muthukrishnan,et al.  AGFS: Adaptive Genetic Fuzzy System for medical data classification , 2014, Appl. Soft Comput..

[14]  Pei-Chann Chang,et al.  A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification , 2011, Appl. Soft Comput..

[15]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[16]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[17]  Susana K. Lai-Yuen,et al.  AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation , 2020, Neurocomputing.

[18]  Xiangyu Chang,et al.  Sparse Regularization in Fuzzy $c$ -Means for High-Dimensional Data Clustering , 2017, IEEE Transactions on Cybernetics.

[19]  Gautam Srivastava,et al.  Neural image reconstruction using a heuristic validation mechanism , 2020, Neural Computing and Applications.

[20]  Alan Wee-Chung Liew,et al.  Multi-label classification via incremental clustering on an evolving data stream , 2019, Pattern Recognit..

[21]  Erkan Kayacan,et al.  A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications , 2019, Neurocomputing.

[22]  Anindya Halder,et al.  R-Ensembler: A greedy rough set based ensemble attribute selection algorithm with kNN imputation for classification of medical data , 2020, Comput. Methods Programs Biomed..

[23]  Okyay Kaynak,et al.  A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology , 2019, J. Frankl. Inst..

[24]  Mohammad Sohel Rahman,et al.  A Random Forest based predictor for medical data classification using feature ranking , 2019, Informatics in Medicine Unlocked.

[25]  Yu Liu,et al.  A new bio-inspired optimisation algorithm: Bird Swarm Algorithm , 2016, J. Exp. Theor. Artif. Intell..

[26]  Rommel M. Barbosa,et al.  Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine , 2020, Medical & Biological Engineering & Computing.

[27]  Dayou Liu,et al.  Evolving support vector machines using fruit fly optimization for medical data classification , 2016, Knowl. Based Syst..

[28]  Haizhou Li,et al.  A Cost-Sensitive Deep Belief Network for Imbalanced Classification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Aditi Sakalle,et al.  Unbalanced breast cancer data classification using novel fitness functions in genetic programming , 2020, Expert Syst. Appl..

[30]  Marcin Wozniak,et al.  Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism , 2017, Symmetry.

[31]  Dongkyoo Shin,et al.  A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms , 2009, 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing.

[32]  Shenghao Yang,et al.  Classification and Prediction of Tibetan Medical Syndrome Based on the Improved BP Neural Network , 2020, IEEE Access.

[33]  Zhenxing Zhang,et al.  Atom search optimization and its application to solve a hydrogeologic parameter estimation problem , 2019, Knowl. Based Syst..

[34]  Himansu Das,et al.  Medical disease analysis using Neuro-Fuzzy with Feature Extraction Model for classification , 2020 .

[35]  G. Lavanya Devi,et al.  A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model , 2020 .

[36]  Aziz Makandar,et al.  Malware analysis and classification using Artificial Neural Network , 2015, 2015 International Conference on Trends in Automation, Communications and Computing Technology (I-TACT-15).

[37]  Yi Lu Murphey,et al.  Multi-class pattern classification using neural networks , 2007, Pattern Recognit..

[38]  Eswaran Perumal,et al.  Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm , 2020, Int. J. Imaging Syst. Technol..