Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms

Lin, Kuan‐Cheng; Hsieh, Yi-Hsiu

doi:10.1007/s10916-015-0306-3

Cited by 27 publications

(10 citation statements)

References 13 publications

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“…These suggested models have been applied for classification of medical data. Lin and Hsieh [51] proposed a hybrid system based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) methods in combination with a support vector machine (SVM) for the selection of optimal feature subsets for the categorization of medical data sets. Muhaideb and Menai [52] proposed a novel mixture metaheuristic that is obtainable for the categorization task of medical data sets.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Inbarani

Azar

et al. 2016

Neural Comput & Applic

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“…These measures are higher as compared to tradition approaches of Machine learning [15]. [24] discussed in their paper that how an accurate feature selection can improve the quality of the prediction model and can increase the accuracy. Feature selection is a method of extracting most significant features that contribute maximum to the output.…”

Section: Literature Reviewmentioning

confidence: 99%

Dual Classification approach for Prediction of Coronary Artery Disease using Gaussian Noise Modeling

2020

IJATCSE

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There are many ways both invasive and non-invasive for the identification of coronary artery disease (CAD). Due to the cost, complexity and requirement of highly skilled medical practitioner with sophisticated laboratory, non-invasive methods like echocardiogram, stress-testing electrocardiogram gained popularity among people. Echocardiography is considered as one of the established technique used for diagnosing heart problems like hardening of heart muscles, blood clots and damaged cardiac tissue. It can also reveal some important estimates to physicians that help them to take correct decisions like ejection fraction, cardiac output and diastolic function. This work presents a dual classification approach where the first phase deals with the clinical data of the patients to diagnose the patients with cardiovascular problems and the second phase deals with the echocardiogram data to identify the probability of survival of the patient. In this dual approach we have used deep learning based models and the results reveals that proposed model is more promising.

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“…Year Algorithm Area of Application Shi, et al [130] 2010 IABAP Global optimization El-Abd [131] 2011 ABC-SPSO Continuous function optimization Kıran and Gündüz [132] 2013 HPA Continuous optimization problems Xiang, et al [133] 2014 PS-MEABC Real parameter optimization Vitorino, et al [134] 2015 ABeePSO Optimization problems Lin and Hsieh [135] 2015 EPSO_ABC Classification of Medical Datasets Using SVMs Zhou and Yang [136] 2015…”

Section: Author/smentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

388

184

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Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

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Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms

Cited by 27 publications

References 13 publications

RETRACTED ARTICLE: Covering-based rough set classification system

RETRACTED ARTICLE: Covering-based rough set classification system

Dual Classification approach for Prediction of Coronary Artery Disease using Gaussian Noise Modeling

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

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