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Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review

Claudia Mazo, Claudia Aura, Arman Rahman, William M. Gallagher, Catherine Mooney
2022 Journal of Personalized Medicine  
Predicting who will have a recurrence and who will not remains challenging, with consequent implications for associated treatment.  ...  Third, representative datasets for breast cancer recurrence are scarce, which hinders model validation and deployment.  ...  metastasis) system is a highly discriminant feature in terms of breast cancer prediction and risk of recurrence.  ... 
doi:10.3390/jpm12091496 pmid:36143281 pmcid:PMC9500690 fatcat:6hzbxcjdengtledaj5mtsiaxdu

Ensemble Classification Algorithms for Breast Cancer Prognosis

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Breast Cancer is the second highest reason for the death rate among women as well as men too in world.  ...  In this paper, we used Data mining classification algorithms to find the presence of breast cancer whether it is benign or malignant and analysis is done on the basics of accuracy and time taken in build  ...  LITERATURE SURVEY Akshya Yadav, Imlikumla Jamir, Raj Rajeshwari Jain, Mayank Sohani [7], survey the Comparsion of Machine learning Algorithms for the breast cancer prediction.  ... 
doi:10.35940/ijitee.b6886.129219 fatcat:eqvt43so2jhl5ej5lb7szidxzu

PREDICTION OF BREAST CANCER STAGES USING MACHINE LEARNING

Gayathri M, Poorviga A., Vasantha Raja S.S S. S.
2021 International journal of electronics engineering and application  
Disease is the normal issue for all individuals on the planet with various kinds. Especially, Breast Cancer is the most regular ailment as a disease type for ladies.  ...  In this paper, two of the most mainstream AI methods have been utilized for characterization of Wisconsin Breast Cancer (Original) dataset and the arrangement execution of these procedures have been contrasted  ...  Ahmad et al. have exercised machine learning algorithms for predicting the rate of two years recurrence of breast cancer disease.  ... 
doi:10.30696/ijeea.ix.i.2021.36-42 fatcat:z6xhcnglxndz3phis4ju2vgt7q

Aggregate Linear Discriminate Analyzed Feature Extraction and Ensemble of Bootstrap with Knn Classifier for Malicious Tumour Detection

2019 International journal of recent technology and engineering  
After this, an ensemble classifier technique is employed to construct malicious and non-malicious tumour classes. The tumour classification based on an ensemble of bagging and knearest neighbour.  ...  Numerical performance evaluations show that 8% improvement by proposed method in tumour classification accuracy for malicious tumour detection in human individuals.  ...  Hybrid Computer-aided diagnosis system for Prediction of Breast Cancer Recurrence (HPBCR) designed in [5] that provided a promising means for prediction of breast cancer recurrence.  ... 
doi:10.35940/ijrte.c4802.098319 fatcat:zq5oswv5grc3biccufrgbruagm

Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy

Simona Rabinovici-Cohen, Xosé M. Fernández, Beatriz Grandal Rejo, Efrat Hexter, Oliver Hijano Cubelos, Juha Pajula, Harri Pölönen, Fabien Reyal, Michal Rosen-Zvi
2022 Cancers  
In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer  ...  We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses.  ...  Acknowledgments: We thank Johan Archinard from Institut Curie for his dedicated continuous support with the IT infrastructure for this study.  ... 
doi:10.3390/cancers14163848 pmid:36010844 pmcid:PMC9405765 fatcat:njcnvpft5basvhwrdzw76vtbhy

Cancer prediction using graph-based gene selection and explainable classifier

Mehrdad Rostami, Mourad Oussalah
2022 Finnish Journal of eHealth and eWelfare  
In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent  ...  Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction.  ...  Case-based ensemble Breast cancer pre- Breast cancer recurrence Low High [17] learning diction cases Nayak et al.  ... 
doi:10.23996/fjhw.111772 fatcat:paqhmluzuzcbhc2suexkmofmva

A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultrasound-Detected Suspicious Breast Masses

Matteo Interlenghi, Christian Salvatore, Veronica Magni, Gabriele Caldara, Elia Schiavon, Andrea Cozzi, Simone Schiaffino, Luca Alessandro Carbonaro, Isabella Castiglioni, Francesco Sardanelli
2022 Diagnostics  
We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision-making towards short-interval follow-up  ...  A balanced image set of biopsy-proven benign (n = 299) and malignant (n = 299) lesions was used for training and cross-validation of ensembles of machine learning algorithms supervised during learning  ...  staging of a known breast cancer; monitoring breast cancers receiving neoadjuvant systemic therapy.  ... 
doi:10.3390/diagnostics12010187 pmid:35054354 pmcid:PMC8774734 fatcat:sj7chyalgjhv3imtyfn3sdxkta

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

Usman Naseem, Junaid Rashid, Liaqat Ali, Jungeun Kim, Qazi Emad Ul Haq, Mazhar Javed Awan, Muhammad Imran
2022 IEEE Access  
It is therefore vital to have a system enabling the healthcare industry to detect breast cancer quickly and accurately.  ...  In this paper, we propose a system for automatic detection of BC diagnosis and prognosis using ensemble of classifiers.  ...  In this paper, our main contributions are: • We presented an ensemble of machine learning-based methods for breast cancer diagnosis and prognosis using an ensemble of machine learning classifiers. • We  ... 
doi:10.1109/access.2022.3174599 fatcat:oih3pv7qvrfoxd75cmahrc3o5y

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations

Asghar Ali Shah, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan
2022 International Journal of Molecular Sciences  
A proposed framework is developed for the early detection of breast adenocarcinoma using an ensemble learning technique with multiple deep learning algorithms, specifically: Long Short-Term Memory (LSTM  ...  Recombination or replication within the gene base ends in a permanent change in the nucleotide collection in a DNA called mutation and some mutations can lead to cancer.  ...  Acknowledgments: The researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project.  ... 
doi:10.3390/ijms231911539 fatcat:t62mkirqwrhipbj6io2hse6z4q

A Machine Learning Ensemble Based on Radiomics to Predict BI-RADS Category and Reduce the Biopsy Rate of Ultra-sound-Detected Suspicious Breast Masses [article]

Matteo Interlenghi, Christian Salvatore, Veronica Magni, Gabriele Caldara, Elia Schiavon, Andrea Cozzi, Simone Schiaffino, Luca Alessandro Carbonaro, Isabella Castiglioni, Francesco Sardanelli
2021 medRxiv   pre-print
We developed a machine learning model based on radiomics to predict the BI-RADS category of ultrasound-detected suspicious breast lesions and support medical decision making towards short-interval follow-up  ...  A balanced image set of biopsy-proven benign (n = 299) and malignant (n = 299) lesions were used for training and cross-validation of ensembles of machine learning algorithms supervised during learning  ...  staging of a known breast cancer; monitoring breast cancers receiving neoadjuvant systemic therapy.  ... 
doi:10.1101/2021.12.16.21267907 fatcat:gsl7nil5izdzxprdknzx6c7tyq

A Multi-Classifier Method based Deep Learning Approach for Breast Cancer
English

Mokhairi Makhtar, Rosaida Rosly, Mohd Khalid Awang, Mumtazimah Mohamad, Aznida Hayati Zakaria
2020 International Journal of Engineering Trends and Technoloy  
In improving the prediction accuracy of breast cancer dataset, this study evaluates the performance of multi-classifier based deep learning approach on datasets.  ...  Medical diagnosis such as breast cancer is considered a significant but complicated task that needs to be carried out correctly and effectively.  ...  Today, the issue of classification of breast cancer is widely used by expert systems and machine learning methods.  ... 
doi:10.14445/22315381/cati3p217 fatcat:ptpfbojaezbnhgxgordrtvxmb4

A Parellel two Stage Classifier for Breast Cancer Prediction and Comparison with Various Ensemble Techniques

Ali Tariq Nagi, Ahmad Wali, Adnan Shahzada, Muhammad Masroor Ahmad
2018 VAWKUM Transactions on Computer Sciences  
Chetan Nashte et al [5] in 2017 explored the possibility of using a machine learning technique for breast cancer prediction in a Clinical Decision Support System (CDSS).  ...  Desta Mulatu et al [6] in 2017 performed a research to identify the best algorithm for the prediction of recurrence of breast cancer. The author reviewed several data mining techniques.  ...  Our research work includes the development of a new technique as well as comparison of the accuracies of various existing machine learning algorithms (ensemble techniques) for breast cancer classification  ... 
doi:10.21015/vtcs.v15i3.523 fatcat:2opeabz4snhvndsip2pkf22sg4

Predication of cancer disease using machine learning approach

F.J. Shaikh, D.S. Rao
2021 Materials Today: Proceedings  
Many of these methods are widely used for the development of predictive models for predicating a cure for cancer, some of the methods are artificial neural networks (ANNs), support vector machine (SVMs  ...  Cancer has identified a diverse condition of several various subtypes.  ...  The authors wanted to predict the possibility of recurrence in patients with breast cancer for five years.  ... 
doi:10.1016/j.matpr.2021.03.625 fatcat:meq7nl6gsfhufozyx62uam7woe

Prediction of lymphedema occurrence in patients with breast cancer using the optimized combination of ensemble learning algorithm and feature selection

Anaram Yaghoobi Notash, Aidin Yaghoobi Notash, Zahra Omidi, Shahpar Haghighat
2022 BMC Medical Informatics and Decision Making  
Method This study was conducted on data of 970 breast cancer patients with lymphedema referred to a lymphedema clinic.  ...  The first phase included data preprocessing, optimizing feature selection for each base learner by the Genetic algorithm, optimizing the combined ensemble learning method, and estimating fitness function  ...  Acknowledgements We thank all patients who had signed the written consent to use data in this project and the Seyed_Khandan Lymphedema Clinic, Tehran, Iran, for contributing to this study.  ... 
doi:10.1186/s12911-022-01937-z pmid:35879760 pmcid:PMC9310496 fatcat:4muldrikqvh35eb6tzvw6lehju

Big Data Analytics to Predict Breast Cancer

Hardi Patel, Dr. Mehul P. Barot
2022 International Journal for Research in Applied Science and Engineering Technology  
This paper compares the machine learning techniques which are used for the prediction of breast cancer. Keywords: Breast Cancer, Malignant, Benign, Machine Learning, Big Data Analytics.  ...  Machine Learning and Data Mining have been widely used in the prediction of breast cancer and on the early detection of breast cancer.  ...  This research is organized as follows; Section 2 introduces of breast cancer. Section 3 explains the algorithms and tools of data mining and machine learning which are used to predict breast cancer.  ... 
doi:10.22214/ijraset.2022.41045 fatcat:uznyzaxhvjeoxllcr4wjtpouny
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