An investigation of XGBoost-based algorithm for breast cancer classification

Liew, Xin Yu; Hameed, Nazia; Clos, Jérémie

doi:10.1016/j.mlwa.2021.100154

Cited by 62 publications

(38 citation statements)

References 35 publications

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“…It is a type of boosting algorithm which combines the output of many weak classifiers by the rule of thumb and produces accurate output. Many authors have conducted multiclass classification problems using Xgboost and the majority of the studies have mentioned the accuracy of Xgboost over other classification algorithms 61 . The Xgboost technique differs from gradient boosting in that the process of adding weak learners does not happen one after the other; instead, it uses a multi‐threaded approach that makes proper use of the CPU core of the machine, resulting in increased speed and performance.…”

Section: Methodsmentioning

confidence: 99%

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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Summary Recent advancements in machine learning techniques are helping researchers to develop predictive models that assist decision‐makers to get a quick, unbiased overview of the processes. But studies using machine learning approaches in analyzing and classifying the injury narratives of the petroleum industries are still in their early stages due to data unavailability and lack of trust in these models. Comparatively, other industries such as construction, manufacturing, aviation and so forth are using the findings from the predictive models but the use of machine learning techniques in analyzing petroleum industry accident data is not gaining much importance. This study aims to use available accident data from the Indian petroleum industry to develop a classification model for predicting possible outcomes of an accident. The data used in this study comprises 194 accident reports with 20 information attributes collected during the 2016–20 period. Six different machine learning algorithms are used to analyze and classify the possible outcome of the accident. It has been found that the Xgboost algorithm has achieved 95% accuracy following multilayer perceptron with 94% accuracy. The rough set theory is also used to extract the indiscernibility relationship between the given attributes causing accident occurrence. The results indicate that “skill‐based error, supervisory violation, no personal protective equipment usage, and lack of standard operating procedure compliance” have contributed to the majority of the accidents. The findings of this study can be used to assist safety professionals in decision‐making, mitigating the root causes of contributing factors.

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Section: Methodsmentioning

confidence: 99%

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Gangadhari

Khanzode

Murthy

2022

Concurrency and Computation

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“…In this research, machine learning techniques have been employed to detect cervical cancer accurately via constructing a framework affected by previous research methods in a similar domain. Research [ 42 ] proves that by utilizing the oversampling process performance of existing approaches can be improved. This research used the random forest to build a classifier predicated on cervical cancer cases.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques

Lilhore

Poongodi

Kaur

et al. 2022

Computational and Mathematical Methods in Medicine

126

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Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model’s primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F 1 – Score , and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.

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“…Liew et al. [38] proposed a technique titled Deep Learning and eXtreme Gradient Boosting (DLXGB) on breast cancer histopathological images using the BreaKHis dataset; the authors applied preprocessing on the dataset, which increased the performance of the network in the binary and multi‐scale classification, thus obtaining an accuracy of 97% in both. However, the authors did not carry out a study about the classification time of the entire process, which compromises the applicability of the technique for applications as a CAD tool.…”

Section: Related Workmentioning

confidence: 99%

New fully automatic approach for tissue identification in histopathological examinations using transfer learning

Santos

Souza

et al. 2022

IET Image Processing

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The use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their changes through diseases. This study aims to develop a tool for classifying histopathological images from breast lesions in the benign and malignant classes through magnification scales by an innovative way of using transfer learning techniques combined with machine learning methods and deep learning. The BreakHis dataset was used in the experiments, consisting of histopathological images of breast cancer with different tumor enlargement scales classified as Malignant or Benign. In this study, various combinations of Extractor-Classifiers were performed, thus seeking to compare the best model. Among the results achieved, the best Extractor-Classifier set formed was CNN DenseNet201, acting as an extractor, with the SVM RBF classifier, obtaining accuracy of 95.39% and precision of 95.43% for the 200X magnification factor. Different models were generated, compared to each other, and validated based on methods in the literature to validate the experiments, thus showing the effectiveness of the proposed model. The proposed method obtained satisfactory results, reaching results in the state-of-the-art for the multi-classification of subclasses from the different scale factors found in the BreakHis dataset and obtaining better results in the classification time.

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An investigation of XGBoost-based algorithm for breast cancer classification

Cited by 62 publications

References 35 publications

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Application of rough set theory and machine learning algorithms in predicting accident outcomes in the Indian petroleum industry

Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques

New fully automatic approach for tissue identification in histopathological examinations using transfer learning

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