Enhanced Monarchy Butterfly Optimization Technique for effective breast cancer diagnosis

Punitha, S.; Amuthan, A.; Joseph, K. Suresh

doi:10.1007/s10916-019-1348-8

Cited by 30 publications

(14 citation statements)

References 21 publications

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“…Where, only [11] is utilized 5 k-fold cross validation used. But in [14] and [15] have utilized only manual data splitting method. However, in [18]- [22], [108] have utilized 10 kfold validation.…”

Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

“…Memon et al [14] suggested machine learning-based diag-nostic system which commendably identifies, the menacing and compassionate people in the environment of IoT, for the early stage identification of the breast cancer diagnosis. They have utilized recursive feature selection algorithm to improve the further precision of obtaining the datasets and building a strong cataloguing for the classification system.…”

Section: A Breast Cancermentioning

confidence: 99%

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Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized stateof-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a cleancut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

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Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

Section: A Breast Cancermentioning

confidence: 99%

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

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“…The MBO algorithm is a type of swarm intelligence metaheuristic algorithm inspired by monarch butterfly migration behavior [12]. The method can be used for medical image segmentation [13,14], feature selection [15][16][17], and classification [18,19].…”

Section: Monarch Butterfly Optimization (Mbo)mentioning

confidence: 99%

An Optimized Framework for Breast Cancer Classification Using Machine Learning

Michael

et al. 2022

BioMed Research International

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Breast cancer, if diagnosed and treated early, has a better chance of surviving. Many studies have shown that a larger number of ultrasound images are generated every day, and the number of radiologists able to analyze this medical data is very limited. This often results in misclassification of breast lesions, resulting in a high false-positive rate. In this article, we propose a computer-aided diagnosis (CAD) system that can automatically generate an optimized algorithm. To train machine learning, we employ 13 features out of 185 available. Five machine learning classifiers were used to classify malignant versus benign tumors. The experimental results revealed Bayesian optimization with a tree-structured Parzen estimator based on a machine learning classifier for 10-fold cross-validation. The LightGBM classifier performs better than the other four classifiers, achieving 99.86% accuracy, 100.0% precision, 99.60% recall, and 99.80% for the FI score.

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“…Extensive studies are conducted on familiar machine learning datasets from UCI Repository. Punitha et al [24] proposed a Intelligent Artificial Bee Colony and Enhanced Monarchy Butterfly Optimization Technique (IABC-EMBOT) to effectively diagnose breast cancer. This depends on two major improvements that, i) concentrates on the alteration of Monarchy Butterfly Optimization (MBO) resulting in enhancement of the degree of assessment on the basis of utilisation rate in the search space, and ii) focuses to remove all constraints of the ABC scheme through increasing search diversification possibilities by remarkable updates made possible by the dynamic and adaptive butterfly operator which enhances the search on the whole.…”

Section: Literature Reviewmentioning

confidence: 99%

Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

Priya¹,

Vadivu

2022

ijhs

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Breast cancer is a major disease identified in women, affecting 2.1 million women every year, and is the reason for most cancer-related mortality in women, as per the World Health Organization (WHO). For cancer researchers, accurately forecasting the life expectancy of breast cancer patients is a serious challenge. Machine Learning (ML) has acknowledged much interest in the hope of providing correct results, but due to irrelevant features, its modelling methodologies and prediction performance are still a difficulty. To solve this issue, Feature Selection (FS) was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Bio-Inspired Ensemble Feature Selection (BIEFS) algorithm is introduced aimed at selecting a subset of features that increase the prediction performance of subsequent classification models while also simplifying their interpretability. BIEFS algorithm uses three feature selection methods such as Adaptive Mutation Enhanced Elephant Herding Optimization (AMEHO), Adaptive Mutation Butterfly Optimization Algorithm (AMBOA), and Adaptive Salp Swarm Algorithm (ASSA) and integrates their normalized outputs for getting quantitative ensemble importance. BIEFS algorithm depends upon the aggregation of multiple FS techniques by Pearson Correlation Coefficient (PCC).This BIEFS algorithm can improve the accuracy of analysis (benign and malignant).

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Enhanced Monarchy Butterfly Optimization Technique for effective breast cancer diagnosis

Cited by 30 publications

References 21 publications

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

An Optimized Framework for Breast Cancer Classification Using Machine Learning

Bio-inspired ensemble feature selection (biefs) and kernel extreme learning machine classifier for breast cancer diagnosis

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