Prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers

Dawngliani, M. S.; Chandrasekaran, Nichith; Lalmuanawma, Samuel; Thangkhanhau, H.

doi:10.1007/978-3-030-46828-6_20

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Besides, Approach-6 with 96.59% of precisions and 97.70% of F1-scores single-handedly, as well as Approach-5 (i.e., DNN only) and Approach-6, both with 98.84% of sensitivities and Approach-6 and Approach-8 (i.e., DNN with Majority Voting), both with 84.62% of specificities also outperform other suggested ensemble approaches and is therefore "Approach-6" is considered to be the proposed ensemble approach. [10] 76.26 ----Sakri et al [11] 81.3 -93.4 63.25 -Chakradeo et al [12] 97.93 93.36 91.00 --Gu et al [13] 91.62 -90.28 -89.39 Goyal et al [14] 85.18 100.0 100.0 100.0 Dawangliani et al [15] 82.80 81.9 82.8 -82.3 Yang et al [16] 97.…”

Section: Proposed Workmentioning

confidence: 99%

“…They resulted in 85.18% accuracy, 100% sensitivity, 100% specificity, 100% precision, and 100% recall. Dawangliani et al [15] developed a prediction of breast cancer recurrence considering ensemble ML approaches on breast cancer datasets. They resulted in 82.807% accuracy, 0.828 Tp rate, 0.534 Fp rates, 81.9% precision, 82.8% recall, 82.3% F-measure, and 79.6% ROC Area.…”

Section: Rana Et Al [9] Introduced a Breast Cancer Diagnosis And Recu...mentioning

confidence: 99%

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Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Ghanashyam Sahoo

2023

IJRITCC

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Predicting progression and deciding on the best follow-up techniques for breast cancer patients is difficult because the illness is diverse and characterized by varying relapse risks. Due to its prevalence, breast cancer has become the top cause of mortality among women worldwide, making diagnosis and prognosis particularly challenging areas of medical study. In addition, the fear of a cancer relapse is a major factor influencing cancer patients' quality of life. The study aims to help doctors determine the likelihood of a breast cancer relapse by applying ensemble learning techniques. In this research, artificial neural networks (ANN) and deep neural networks (DNN) ensembled with Weighted averaging, minority, and majority voting approaches have been investigated for performance enhancements on the breast cancer recurrence dataset sourced from the UCI-ML repository. The empirical analysis shows that this ensemble learning-enabled proposed novel approach shows improved accuracy, precision, sensitivity, specificity, and F1-score of 96.21%, 96.59%, 98.84%, 84.62%, and 97.41%, respectively. The findings of this study can aid doctors in making more informed treatment decisions, thereby improving patient outcomes.

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Section: Proposed Workmentioning

confidence: 99%

Section: Rana Et Al [9] Introduced a Breast Cancer Diagnosis And Recu...mentioning

confidence: 99%

Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Ghanashyam Sahoo

2023

IJRITCC

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“…Their results showed a perfect score of 85.18 percent accuracy, a perfect score of 100% sensitivity, and a perfect score of 100% specificity. Dawangliani et al [12] proposed a prediction system using ML methods applied to breast cancer datasets. These factors influenced the following outcomes: ROC area: 79.6%, accuracy: 81.9%, recall: 82.8%, time to peak: 0.828, and false positive rate: 0.534.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Sahoo,

Nayak,

Tripathy

et al. 2024

IJEECS

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Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substantial influence on the quality of life of cancer patients is the fear that they may experience a relapse of their disease. The objective of the study is to give medical practitioners a more effective strategy for using ensemble learning techniques to forecast when breast cancer may recur. This research aimed to investigate the usage of deep neural networks (DNNs) and artificial neural networks (ANNs) in addition to machine learning (ML) based approaches, including bagging, averaging, and voting, to enhance the efficacy of breast cancer relapse diagnosis on two breast cancer relapse datasets. Results from the empirical study demonstrate that the proposed ensemble learning-enabled approach improves accuracies by 96.31% and 95.81%, precisions by 96.70% and 96.15%, sensitivities by 98.88% and 98.68%, specificities by 84.62% in both, F1-scores by 97.78% and 97.40%, and area under the curve (AUCs) of 0.987 and 0.978, with University Medical Centre, Institute of Oncology (UMCIO) and Wisconsin prognostic breast cancer (WPBC) datasets respectively. Consequently, these improved disease outcomes may encourage physicians to use this model to make better treatment choices.

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“…It is claimed that these technologies may minimize diagnostic errors and discrepancies among observers at any level of prediction, prognosis, and treatment. Therefore, diagnostic and prognostic models can help identify at-risk patients and adopt the most effective support and treatment programs (3,(19)(20)(21). Machine learning (ML), a branch of AI, can extract high-quality knowledge and patterns from a substantial raw dataset.…”

Section: Introductionmentioning

confidence: 99%

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

et al. 2022

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Background: Early screening and diagnosis of breast cancer (BC) is critical for improving the quality of care and reducing the mortality rate. Objectives: This study aimed to construct and compare the performance of several machine learning (ML) algorithms in predicting BC. Methods: This descriptive and applied study included 1,052 samples (442 BC and 710 non-BC) with 30 features related to positive and negative BC diagnoses. The data mining (DM) process was implemented using the selected algorithm, including J-48 and random forest (RF) decision tree (DT), multilayer perceptron (MLP), Naïve Bayes (NB), Adaboost (AB), and logistics regression (LR) classifier. Then, we obtained the best algorithm by comparing their performances using the confusion matrix and area under the receiver operator characteristics (ROC) curve (AUC). Finally, we adopted the best model for BC prognosis. Results: The results of evaluating various DM algorithms revealed that the J-48 DT algorithm had the best performance (AUC = 0.922), followed by the AB, MLP, LR, and RF algorithms (AUC: 0.899, 0819, 0.716, and 0.703, respectively). Also, the NB algorithm achieved the lowest performance in this regard (AUC = 0.669). Conclusions: The ML presents a reasonable level of accuracy for an early diagnosis and screening of breast malignancies. Also, the empirical results showed that the J-48 DT algorithm yielded higher performance than other classifiers.

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Prediction of Breast Cancer Recurrence Using Ensemble Machine Learning Classifiers

Cited by 6 publications

References 9 publications

Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Enhanced breast Cancer Relapse Prediction Based on Ensemble Learning Approaches

Breast cancer relapse disease prediction improvements with ensemble learning approaches

Comparing Data Mining Algorithms for Breast Cancer Diagnosis

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