Artificial intelligence based medical decision support system for early and accurate breast cancer prediction
Feature selection, which picks the optimal subset of characteristics related to the target data
by deleting unnecessary data, is one of the most important aspects of the machine learning …
by deleting unnecessary data, is one of the most important aspects of the machine learning …
A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction
Feature selection is one of the crucial data preprocessing techniques for improving the
performance of machine learning (ML) models. Recently, metaheuristic feature selection …
performance of machine learning (ML) models. Recently, metaheuristic feature selection …
A two-stage feature selection approach using hybrid quasi-opposition self-adaptive coati optimization algorithm for breast cancer classification
K Thirumoorthy - Applied Soft Computing, 2023 - Elsevier
Breast cancer (BC) is one of the leading causes of high mortality rates among women. An
early disease diagnosis is crucial in breast cancer's treatment for improving the survival rate …
early disease diagnosis is crucial in breast cancer's treatment for improving the survival rate …
Breast cancer detection using particle swarm optimization and decision tree machine learning technique
Cancer of the breast is one of the deadliest diseases encountered by women and requires
early diagnosis. Although more time is required for the traditional diagnostic process, a …
early diagnosis. Although more time is required for the traditional diagnostic process, a …
Multimodal particle swarm optimization for feature selection
The purpose of feature selection (FS) is to eliminate redundant and irrelevant features and
leave useful features for classification, which can not only reduce the cost of classification …
leave useful features for classification, which can not only reduce the cost of classification …
Efficient feature selection for breast cancer classification using soft computing approach: A novel clinical decision support system
One of the essential data pre-processing methods for enhancing the performance of
machine learning (ML) models is feature selection. Because they choose the most optimal …
machine learning (ML) models is feature selection. Because they choose the most optimal …
An oscillatory particle swarm optimization feature selection algorithm for hybrid data based on mutual information entropy
J He, L Qu, P Wang, Z Li - Applied Soft Computing, 2024 - Elsevier
Hybrid data lead to overfitting in machine learning models, which may reduce the accuracy
of classification. Feature selection can not only reduce the computational cost of processing …
of classification. Feature selection can not only reduce the computational cost of processing …
An enhanced soft-computing based strategy for efficient feature selection for timely breast cancer prediction: Wisconsin Diagnostic Breast Cancer dataset case
When contemplating the improvement of overall performance in machine learning (ML)
models, a critical strategy for optimizing data preparation is feature selection (FS). There has …
models, a critical strategy for optimizing data preparation is feature selection (FS). There has …
An evolving feature weighting framework for radial basis function neural network models
Abstract Via Granular Computing (GrC), one can create effective computational frameworks
for obtaining information from data, motivated by the human perception of combining similar …
for obtaining information from data, motivated by the human perception of combining similar …