Breast cancer diagnosis and recurrence prediction using machine learning techniques. This paper introduces a novel hybrid computational framework that combines the predictive power of machine learning (ML) with the mathematical formalism of Neutrosophic Sets for uncertainty quantification. The aim of this research is mainly to use machine learning 2 days ago · In this study, machine learning models trained on data for a specific type of cancer were employed to predict three-year survival after diagnosis for other cancer types. Oct 30, 2023 · Simple SummaryArtificial intelligence (AI) has seamlessly integrated into the medical field, especially in diagnostic imaging, thanks to ongoing AI advancements. These findings demonstrate … Breast Cancer is most significant disease of death for women in the world, early identification is very essential. Abstract: Data analytics play vital roles in diagnosis and treatment in the health care sector. May 13, 2023 · Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. We propose Dec 31, 2025 · The aim of this study was to develop a predictive model based on machine learning to predict the probability of breast cancer developing bone metastasis. To enable practitioner decisionmaking, huge volumes of data should be processed with machine Strong prognostic techniques that can handle the inherent ambiguities, uncertainties, and inconsistent data common in clinical practice are essential for managing breast cancer. Breast Cancer Prediction Using Machine Learning • Explored breast cancer dataset with EDA & visualizations. . • Preprocessed data by handling nulls, encoding diagnosis labels, and scaling features. Biopsies, though more reliable, are Nov 20, 2025 · Breast cancer recurrence remains a critical concern in oncology, requiring accurate predictive models to enhance patient outcomes. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer The aim of this research is mainly to use machine learning methods for forecasting significant characteristics related to breast cancer using the data to facilitate diagnosis and treatment accordingly, and to find significant associations and establish a premise for enhancing patient prognosis and the accuracy of cancer therapy. The components obtained will be sent to the SVM which classifies the cancer based on Multi-Level and helps in prediction of malignancy of cancer, the early dangerous stage will urge clinical Jan 31, 2022 · A prediction model is proposed, which is specifically designed for prediction of Breast Cancer using Machine learning algorithms Decision tree classifier, Naïve Bayes, SVM and KNearest Neighbour algorithms. Feb 11, 2025 · The review explored machine learning in cancer prediction/prognosis, emphasizing recent studies using supervised ML and classification algorithms to predict disease outcomes. Methods: Data of breast cancer patients diagnosed between 2010 and 2015 were extracted from The Surveillance, Epidemiology, and End Results (SEER) database. May 8, 2025 · This LASSO-enhanced ensemble model significantly improves the accuracy and interpretability of breast cancer recurrence prediction. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. The prior goal of the proposed May 8, 2025 · This study aimed to develop a machine learning-based breast cancer local recurrence risk prediction model by integrating extensive clinical data and advanced data analysis techniques, with the goal of improving risk assessment and supporting personalized treatment decisions. In order to enhance the precision and dependability of Breast Cancer recurrences diagnosis, this study investigates the application of several machine learning techniques, such as Support Vector Machine, Genetic techniques, and Voting Classifier. Machine learning algorithms remove human limitations, offering more accuracy in diagnosing diseases like cancer. By identifying individualized recurrence risks through SHAP analysis, the model supports more precise, data-driven clinical decision-making. Breast cancer, the second most diagnosed cancer in women, often relies on mammography, which is only 70 % accurate, leading to potential misdiagnosis. With the continuous development of computer technology, more and more attention has been paid to computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images, which are available for all breast cancer patients undergone surgical treatment. Jan 1, 2025 · In bioinformatics, the integration of machine learning has revolutionized disease diagnosis. This study presents a comparative analysis of machine learning and deep learning techniques for predicting breast cancer recurrence Jan 29, 2025 · This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. xuletpg ejz xxyrc dapbyo zenom fjpoma fsfurc hosx twcxlj woa