A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection

Deng, Clemen; Perkowski, Marek

doi:10.1109/ismvl.2015.27

Cited by 15 publications

(5 citation statements)

References 21 publications

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“…Alternative methods are ranked to determine the best approach [ 90 , 91 ]. Such techniques include weighted sum model [92] , multiplicative exponential weighting [93] , weighted product method [94] , hierarchical adaptive weighting [95] , simple additive weighting [96] , technique for order of preference by similarity to ideal solution [97] , vlsekriterijumska optimizacija i kompromisno resenje [98] , analytic hierarchy process (AHP) [99] , novel technique for the reorganisation of opinion order to interval levels (TROOIL) [49] and preference selection index (PSI) [100] . The major drawbacks of all of these methods, except for AHP, TROOIL and PSI, include the absence of weight generation provision [101][102][103].…”

Section: Development Of Prioritisation Approach Via Mcdm Methodsmentioning

confidence: 99%

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Albahri

Al‐Obaidi

Zaidan

et al. 2020

Computer Methods and Programs in Biomedicine

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Context: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19. Objective: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

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Section: Development Of Prioritisation Approach Via Mcdm Methodsmentioning

confidence: 99%

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Albahri

Al‐Obaidi

Zaidan

et al. 2020

Computer Methods and Programs in Biomedicine

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“…Therefore, LPSVM exhibits the highest performance. Deng et al [27] utilized a novel technique called the weighted hierarchical adaptive voting ensemble (WHAVE). They contrasted WHAVE's precision with seven other techniques that had the best precisions in earlier studies.…”

Section: A Clinical Breast Cancer Datasetsmentioning

confidence: 99%

An Explainable Artificial Intelligence Model for the Classification of Breast Cancer

Khater,

Hussain,

Bendardaf

et al. 2024

IEEE Access

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Breast cancer is the most common cancer among women and globally affects both genders. The disease arises due to abnormal growth of tissue formed of malignant cells. Early detection of breast cancer is crucial for enhancing the survival rate. Therefore, artificial intelligence has revolutionized healthcare and can serve as a promising tool for early diagnosis. The present study aims to develop a machine-learning model to classify breast cancer and to provide explanations for the model results. This could improve the understanding of the diagnosis and treatment of breast cancer by identifying the most important features of breast cancer tumors and the way they affect the classification task. The bestperforming machine-learning model has achieved an accuracy of 97.7% using k-nearest neighbors and a precision of 98.2% based on the Wisconsin breast cancer dataset and an accuracy of 98.6% using the artificial neural network with 94.4% precision based on the Wisconsin diagnostic breast cancer dataset. Hence, this asserts the importance and effectiveness of the proposed approach. The present research explains the model behavior using model-agnostic methods, demonstrating that the bare nuclei feature in the Wisconsin breast cancer dataset and the area's worst feature Wisconsin diagnostic breast cancer dataset are the most important factors in determining breast cancer malignancy. The work provides extensive insights into the particular characteristics of the diagnosis of breast cancer and suggests possible directions for expected investigation in the future into the fundamental biological mechanisms that underlie the disease's onset. The findings underline the potential of machine learning to enhance breast cancer diagnosis and therapy planning while emphasizing the importance of interpretability and transparency in artificial intelligence-based healthcare systems.

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“…Ensemble learning is to get several classifiers (called basic learners) in the process of training samples, and then combine these basic learners in a certain way, so that multiple basic learners work together to solve a learning task. For the combination of basic learners, the traditional methods are generally as follows: majority voting method [9], weight voting method [10], hierarchical combination method [11], etc. These integrated learning algorithms can effectively solve all kinds of classification and prediction problems, but they still have shortcomings.…”

Section: Related Workmentioning

confidence: 99%

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

2020

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In the context power big data, the household power consumption data on the user side has the characteristics of large quantity, wide distribution and many types. Ensemble learning is very excellent in the analysis and mining of power data with large amount of data, strong timeliness and many influencing factors. Based on the data of household power consumption, this paper analyzes and predicts the power consumption of some users in a city by using selective ensemble learning technology and combining with meteorological factors. In this paper, K-means algorithm is used to cluster the household power consumption, and then the clustering results are combined with the meteorological information. In the stage of power consumption prediction, a Filter Iterative Optimization Ensemble Strategy (FIOES) is proposed to selectively ensemble the basic learners and get the final prediction model. Experimental results show that the FIOES algorithm has better performance in time cost and prediction accuracy than the traditional ensemble learning algorithm. INDEX TERMS Power big data, ensemble learning, power consumption, iterative optimization.

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A Novel Weighted Hierarchical Adaptive Voting Ensemble Machine Learning Method for Breast Cancer Detection

Cited by 15 publications

References 21 publications

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

Helping doctors hasten COVID-19 treatment: Towards a rescue framework for the transfusion of best convalescent plasma to the most critical patients based on biological requirements via ml and novel MCDM methods

An Explainable Artificial Intelligence Model for the Classification of Breast Cancer

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

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