Efficient Breast Cancer Prediction Using Ensemble Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

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Comparative Study of Different Machine Learning Models for Breast Cancer Diagnosis

Innovations in Soft Computing and Information Technology ◽

10.1007/978-981-13-3185-5_3 ◽

2019 ◽

pp. 17-25 ◽

Cited By ~ 2

Author(s):

Aman Kumar ◽

M. Poonkodi

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Comparative Study ◽

Cancer Diagnosis ◽

Breast Cancer Diagnosis ◽

Learning Models ◽

Machine Learning Models

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Potential Breast Cancer Drug Prediction using Machine Learning Models

2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) ◽

10.1109/ic-etite47903.2020.288 ◽

2020 ◽

Author(s):

N. Priya ◽

G. Shobana

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Cancer Drug ◽

Learning Models ◽

Machine Learning Models

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Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Cancers ◽

10.3390/cancers13236013 ◽

2021 ◽

Vol 13 (23) ◽

pp. 6013

Author(s):

Hyun-Soo Park ◽

Kwang-sig Lee ◽

Bo-Kyoung Seo ◽

Eun-Sil Kim ◽

Kyu-Ran Cho ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Random Forest ◽

Cancer Patients ◽

Low Dose ◽

Random Forest Model ◽

Breast Cancer Patients ◽

Learning Models ◽

Forest Model ◽

Machine Learning Models

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

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Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.757111 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yadi Zhu ◽

Ling Yang ◽

Hailin Shen

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Lymph Node ◽

Sentinel Lymph Node ◽

Breast Cancer Patients ◽

Learning Models ◽

Preoperative Prediction ◽

Machine Learning Model ◽

Validation Set ◽

Machine Learning Models

PurposeTo explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.MethodsThe clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.ResultsThere is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.ConclusionsWe revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.

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