Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review
A Ghavidel, P Pazos - Journal of Cancer Survivorship, 2023 - Springer
Abstract Knowledge discovery in databases (KDD) is crucial in analyzing data to extract
valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in …
valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in …
An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis
T Tran, U Le, Y Shi - Plos one, 2022 - journals.plos.org
Early detection of breast cancer plays a critical role in successful treatment that saves
thousands of lives of patients every year. Despite massive clinical data have been collected …
thousands of lives of patients every year. Despite massive clinical data have been collected …
Fusion model for classification performance optimization in a highly imbalance breast cancer dataset
Accurate diagnosis of breast cancer using automated algorithms continues to be a
challenge in the literature. Although researchers have conducted a great deal of work to …
challenge in the literature. Although researchers have conducted a great deal of work to …
[PDF][PDF] Predicting breast cancer via supervised machine learning methods on class imbalanced data
K Rajendran, M Jayabalan… - International Journal of …, 2020 - researchgate.net
A widespread global health concern among women is the incidence of the second most
leading cause of fatality which is breast cancer. Predicting the occurrence of breast cancer …
leading cause of fatality which is breast cancer. Predicting the occurrence of breast cancer …
[HTML][HTML] The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification
Breast cancer is one of the most common causes of death among women, and early
diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used …
diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used …
[Retracted] Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches
Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous
studies have been conducted to predict survival markers, although the majority of these …
studies have been conducted to predict survival markers, although the majority of these …
On combining feature selection and over-sampling techniques for breast cancer prediction
MW Huang, CH Chiu, CF Tsai, WC Lin - Applied Sciences, 2021 - mdpi.com
Breast cancer prediction datasets are usually class imbalanced, where the number of data
samples in the malignant and benign patient classes are significantly different. Over …
samples in the malignant and benign patient classes are significantly different. Over …
Classification of breast cancer risk factors using several resampling approaches
MF Kabir, S Ludwig - 2018 17th IEEE International Conference …, 2018 - ieeexplore.ieee.org
Breast cancer is the most common cancer in women worldwide and the second most
common cancer overall. Predicting the risk of breast cancer occurrence is an important …
common cancer overall. Predicting the risk of breast cancer occurrence is an important …
An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data
Background Breast cancer is one of the most critical cancers and is a major cause of cancer
death among women. It is essential to know the survivability of the patients in order to ease …
death among women. It is essential to know the survivability of the patients in order to ease …
Application of artificial intelligence techniques to predict risk of recurrence of breast cancer: a systematic review
Breast cancer is the most common disease among women, with over 2.1 million new
diagnoses each year worldwide. About 30% of patients initially presenting with early stage …
diagnoses each year worldwide. About 30% of patients initially presenting with early stage …