Prediction of Breast Cancer using Machine Learning Approaches

Abstract

Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Radiomics; Contemporary Applications in the Management of Anal Cancer; A Systematic Review

“…16,17,22,23 This is precisely where the prowess of machine learning (ML) algorithms becomes evident, having already demonstrated remarkable accomplishments across various cancer categories. [32][33][34][35] A proportion of ASCC patients undergoing curative CRT still require salvage abdominoperineal resection (APR) due to inadequate treatment response or local recurrence. 36,37 In this context, the integration of radiomics and ML from outset imaging may play a significant role in stratifying patients who are at higher risk of requiring salvage APR.…”

A Review on Prediction Model for Breast Cancer Using Machine Learning

“…The essence of this analysis is to accurately classify recurring and non-recurring events. [5] suggested that breast cancer may be predicted using machine learning. This is so that prompt remedial action may be taken to prevent mortality and help the disease advance more slowly.…”

“…There are many techniques that have been introduced in the field of breast cancer prediction. Techniques such as deep learning [4], machine learning [5], and artificial intelligence [6]. Several ML techniques, including Support Vector Machine [7], Genetic [8], and classification techniques, are used to predict breast cancer.…”

Machine Learning-Based Hybrid Recommendation (SVOF-KNN) Model For Breast Cancer Coimbra Dataset Diagnosis

“…of broadcast. So, the initial and appropriate treatment of cancer (breast) is essential [15] [16]. The large quantity of fatty and fibrous tissues of BC initializes irregular evolution which becomes the reason for BC.…”