An overview on the advancements of support vector machine models in healthcare applications: a review

R Guido, S Ferrisi, D Lofaro, D Conforti - Information, 2024‏ - mdpi.com
Support vector machines (SVMs) are well-known machine learning algorithms for
classification and regression applications. In the healthcare domain, they have been used …

[HTML][HTML] Machine learning, IoT and 5G technologies for breast cancer studies: A review

HE Saroğlu, I Shayea, B Saoud, MH Azmi… - Alexandria engineering …, 2024‏ - Elsevier
Cancer is a life-threatening ailment characterized by the uncontrolled proliferation of cells.
Breast cancer (BC) represents the most highly infiltrative neoplasms and constitutes the …

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

AS Elkorany, ZF Elsharkawy - Scientific Reports, 2023‏ - nature.com
Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be
saved by its early discovery. Mammography is frequently used to diagnose BC. The …

A novel enhanced hybrid clinical decision support system for accurate breast cancer prediction

LK Singh, M Khanna - Measurement, 2023‏ - Elsevier
Feature selection is one of the crucial data preprocessing techniques for improving the
performance of machine learning (ML) models. Recently, metaheuristic feature selection …

[HTML][HTML] A light gradient-boosting machine algorithm with tree-structured parzen estimator for breast cancer diagnosis

TO Omotehinwa, DO Oyewola, EG Dada - Healthcare Analytics, 2023‏ - Elsevier
Breast cancer is a common and potentially life-threatening disease. Early and accurate
diagnosis of breast cancer is crucial for effective treatment and improved patient outcomes …

Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO)

S Almutairi, S Manimurugan, BG Kim… - Applied Soft …, 2023‏ - Elsevier
Breast cancer (BC) is a primary reason for death among the female population around the
world. Early identification can aid in decreasing the mortality rates associated with this …

Toward improving breast cancer classification using an adaptive voting ensemble learning algorithm

A Batool, YC Byun - IEEE Access, 2024‏ - ieeexplore.ieee.org
Over the past decade, breast cancer has been the most common type of cancer in women.
Different methods were proposed for breast cancer detection. These methods mainly classify …

An enhanced soft-computing based strategy for efficient feature selection for timely breast cancer prediction: Wisconsin Diagnostic Breast Cancer dataset case

LK Singh, M Khanna, R Singh - Multimedia Tools and Applications, 2024‏ - Springer
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 …

Machine learning to predict the adsorption capacity of microplastics

G Astray, A Soria-Lopez, E Barreiro, JC Mejuto… - Nanomaterials, 2023‏ - mdpi.com
Nowadays, there is an extensive production and use of plastic materials for different
industrial activities. These plastics, either from their primary production sources or through …

Multi-input deep learning approach for breast cancer screening using thermal infrared imaging and clinical data

D Tsietso, A Yahya, R Samikannu, MU Tariq… - IEEE …, 2023‏ - ieeexplore.ieee.org
Breast cancer is one of the most prevalent causes of death among women across the globe.
Early detection is the best strategy for reducing the mortality rate. Currently, mammography …