An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

Naseem, Usman; Rashid, Junaid; Ali, Liaqat; Kim, Jungeun; Qazi, Emad-ul-Haq; Awan, Mazhar Javed; Imran, Muhammad

doi:10.1109/access.2022.3174599

Cited by 57 publications

(22 citation statements)

References 41 publications

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“…We summarize some of the recent machine learning, deep learning, ensemble learning techniques based on the breast cancer classification in Table 1. [2-4, 6, 9, 12, 14, 18, 20, 23, 24, 27-33, 36, 40, 47, 48] or textual datasets [1,5,10,17,21,34,41,42], and the researchers are working on them [1-6, 9, 12, 14, 17, 18, 20, 21, 23, 24, 27-34, 36, 40-42, 47, 48]. The existing works on classification are using the datasets with fewer number of sample images [1, 3, 5, 6, 17, 18, 21, 24, 28-30, 33, 34, 36, 41, 42, 47] that may not sufficient to train deep learning algorithms because training process of a deep learning network require a large amount of image data.…”

Section: Related Workmentioning

confidence: 99%

“…The existing works on classification are using the datasets with fewer number of sample images [1, 3, 5, 6, 17, 18, 21, 24, 28-30, 33, 34, 36, 41, 42, 47] that may not sufficient to train deep learning algorithms because training process of a deep learning network require a large amount of image data. The most frequently used datasets are the BreakHis [2,4,6,9,12,14,23,27,31,32,40,48] and the WBCD [1,5,10,17,21,34,41,42]. However, the WBCD dataset consists of only 569 or 699 instances with 32 features, while, the BreakHis dataset consists of 7909 images.…”

Section: Related Workmentioning

confidence: 99%

“…However, the WBCD dataset consists of only 569 or 699 instances with 32 features, while, the BreakHis dataset consists of 7909 images. Therefore, the BreakHis dataset [2, 4, 9, 12, 14, 20, 23, 27-29, 31, 32, 40, 47, 48] and WBCD dataset [1,5,10,17,21,21,34,41,42] can be the most suitable dataset with a sufficient number of breast cancer images. SELF ❼ The researchers have adopted machine learning [1,6,10,24,29] or deep learning [2,4,12,14,18,28,31,40,47,48] or ensemble learning [3, 5, 17, 20, 21, 23, 27, 30, 32-34, 36, 41, 42] techniques to address the breast cancer classification problem, and put their best efforts to improve the performance of their proposed approach(s).…”

Section: Related Workmentioning

confidence: 99%

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SELF: a stacked-based ensemble learning framework for breast cancer classification

Jakhar

Singh

2023

Evol. Intel.

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Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Among them, breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, there are about 2.3 million cases and 685000 deaths have been reported all over the world. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machinebased smart breast cancer diagnosis system that can detect cancer at its early stage and be cured more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stackedbased ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computeraided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images obtained on 82 patients and Wisconsin Breast SELF Cancer Database (WBCD) with 569 instances for the performance evaluation. We have trained several distinct classifiers on both datasets and selected the best five common classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, Adaboost, Gradient Boosting, and KNN9 classifiers as the base learners, and logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets, and achieved the testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on BreakHis dataset also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores with values of 94.17%, 89.41%, and 80.81% respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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SELF: a stacked-based ensemble learning framework for breast cancer classification

Jakhar

Singh

2023

Evol. Intel.

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“…Data science, bioinformatics, statistical genomics, and other fields have all attempted to address this issue. In addition to the aforementioned medical images and genomic data, these apps also allow for the study of organized data [ 29 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

et al. 2022

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Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making.

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“…Experimental results on WDBC dataset showed that the accuracy of HAW-RP was 98.5%. U. Naseem et al [17] proposed a classifier integration-based breast cancer diagnosis system and automatic prognostic detection system. Experimental results on the WDBC dataset showed that the integrated method outperformed other single methods, with an accuracy of 98.83%.…”

mentioning

confidence: 99%

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

Chen,

Mei,

Zhou

et al. 2023

IEEE Access

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Breast cancer has replaced lung cancer as the number one cancer among women worldwide. In this paper, we take breast cancer as the research object, and pioneer a hybrid strategy to process the data, and combine the machine learning method to build a more accurate and efficient breast cancer auxiliary diagnosis model. First, the combined sampling method SMOTE-ENN is used to solve the problem of sample imbalance, and the data are standardized to make the data have better separability. Then, the features of the dataset are initially screened using the mutual information method, and further secondary feature selection is performed using the recursive feature elimination method based on the XGBoost algorithm. Thus, the feature dimensionality of the dataset is reduced and the generalization ability of the model is improved. Finally, five different machine learning models are used for classification prediction, the best combination of parameters for each model is found using a grid search method, and the final results of each model are derived using a 10-fold cross-validation method. The experiments are conducted using the Wisconsin Diagnostic Breast Cancer dataset (WDBC), and the results of the study find that after the data are processed by the hybrid strategy, the best prediction results are obtained using the RF model with 99.52% accuracy, which is better than the previous research methods.

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An Automatic Detection of Breast Cancer Diagnosis and Prognosis Based on Machine Learning Using Ensemble of Classifiers

Cited by 57 publications

References 41 publications

SELF: a stacked-based ensemble learning framework for breast cancer classification

SELF: a stacked-based ensemble learning framework for breast cancer classification

A Healthcare Paradigm for Deriving Knowledge Using Online Consumers’ Feedback

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

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