Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Murugesan, Shibi; Bhuvaneswaran, R. S.; Nehemiah, H. Khanna; Subbiah, Sankari; Jane, Y. Nancy

doi:10.1155/2021/6662420

Cited by 25 publications

(18 citation statements)

References 60 publications

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“…Performance metrics used for evaluation are found using Eqs. (5) to (8) It is observed from the results that the accuracy obtained is 14% higher than the accuracy in CSO and approximately 2% higher than KH and BFO. Similarly, precision and recall seem higher in the proposed than in existing CSO, KH, and BFO algorithms.…”

Section: Optimal Feature Selection Using Grey Wolf Optimizermentioning

confidence: 80%

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Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Bhuvaneshwari¹,

Ahmed²,

Masud³

et al. 2023

Computer Systems Science and Engineering

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The immediate and quick spread of the coronavirus has become a life-threatening disease around the globe. The widespread illness has dramatically changed almost all sectors, moving from offline to online, resulting in a new normal lifestyle for people. The impact of coronavirus is tremendous in the healthcare sector, which has experienced a decline in the first quarter of 2020. This pandemic has created an urge to use computer-aided diagnosis techniques for classifying the Covid-19 dataset to reduce the burden of clinical results. The current situation motivated me to choose correlationbased development called correlation-based grey wolf optimizer to perform accurate classification. A proposed multistage model helps to identify Covid from Computed Tomography (CT) scan image. The first process uses a convolutional neural network (CNN) for extracting the feature from the CT scans. The Pearson coefficient filter method is applied to remove redundant and irrelevant features. Finally, the Grey wolf optimizer is used to choose optimal features. Experimental analysis proves that this determines the optimal characteristics to detect the deadly disease. The proposed model's accuracy is 14% higher than the krill herd and bacterial foraging optimization for severe accurate respiratory syndrome image (SARS-CoV-2 CT) dataset. The COVID CT image dataset is 22% higher than the existing krill herd and bacterial foraging optimization techniques. The proposed techniques help to increase the classification accuracy of the algorithm in most cases, which marks the stability of the stated result. Comparative analysis reveals that the proposed classification technique to predict COVID-19 with maximum accuracy of 98% outperforms other competitive approaches.

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Section: Optimal Feature Selection Using Grey Wolf Optimizermentioning

confidence: 80%

“…Support Vector Machines are widely used to classify more diseases in the medical field. Murugesan et al [8] employed a super learner to detect the disease. A wrapper approach was developed using the cat optimization algorithm (CSO), bacterial foraging (BFO), and krill herd (KH) optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Bhuvaneshwari¹,

Ahmed²,

Masud³

et al. 2023

Computer Systems Science and Engineering

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“…(d) For Indian liver dataset, the study [57] reduces 90% data and attains 71.68% CA but the present study results in 50% data reduction and 72.14% CA. On the other hand, the work [55] yields only 70% CA without removing any feature.…”

Section: Discussion On the Experimental Resultsmentioning

confidence: 98%

A Hybrid Feature Reduction Approach for Medical Decision Support System

Kar

Sarkar

2022

Mathematical Problems in Engineering

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Feature reduction is essential at the preprocessing stage of designing any reliable and fast disease diagnosis model. Addressing the limitations like disease specificity, information loss, and operating NP problem in polynomial time, this paper introduces a two-step hybrid feature selection approach to identify a subset of most relevant and contributing features of each medical dataset for constructing diagnostic model. The concept of information gain is used in Step I to select the informative features, whereas a correlation coefficient-based approach is employed in Step II to retain the informative features possessing much dependency with class attribute but less dependency among the non-class attributes. In particular, both the approaches are sequentially fused to select approximately optimal features in order to construct better classification model in terms of performance and time. The optimal threshold criteria are decided to choose the most appropriate features from the datasets. The effectiveness of the proposed approach is assessed using six individual competent learners and one ensemble learner over seventeen disease datasets of smaller to larger dimensions. The empirical results indicate that the proposed approach improves the performance over the datasets after feature selection, reducing considerable amount of irrelevant and redundant data.

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“…To simplify the research process, statistical models are now often used. These models, created on the basis of a selected data set, are often constructed to predict or classify [ 121 , 122 , 123 , 124 ] collected data. In the presented work, the information reflecting the structure of the enamel was used to develop a model estimating the day of postnatal life.…”

Section: Discussionmentioning

confidence: 99%

Surface and Structural Studies of Age-Related Changes in Dental Enamel: An Animal Model

et al. 2022

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In the animal kingdom, continuously erupting incisors provided an attractive model for studying the enamel matrix and mineral composition of teeth during development. Enamel, the hardest mineral tissue in the vertebrates, is a tissue sensitive to external conditions, reflecting various disturbances in its structure. The developing dental enamel was monitored in a series of incisor samples extending the first four weeks of postnatal life in the spiny mouse. The age-dependent changes in enamel surface morphology in the micrometre and nanometre-scale and a qualitative assessment of its mechanical features were examined by applying scanning electron microscopy (SEM) and atomic force microscopy (AFM). At the same time, structural studies using XRD and vibrational spectroscopy made it possible to assess crystallinity and carbonate content in enamel mineral composition. Finally, a model for predicting the maturation based on chemical composition and structural factors was constructed using artificial neural networks (ANNs). The research presented here can extend the existing knowledge by proposing a pattern of enamel development that could be used as a comparative material in environmental, nutritional, and pharmaceutical research.

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Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Cited by 25 publications

References 60 publications

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

Covid-19 Detection Using Deep Correlation-Grey Wolf Optimizer

A Hybrid Feature Reduction Approach for Medical Decision Support System

Surface and Structural Studies of Age-Related Changes in Dental Enamel: An Animal Model

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