Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer DOI Creative Commons

José Manuel Martínez-Ramírez,

C. J. Carmona, Marı́a Jesús Ramı́rez-Expósito

et al.

Life, Journal Year: 2025, Volume and Issue: 15(2), P. 211 - 211

Published: Jan. 31, 2025

This study explored the application of explainable machine learning models to enhance breast cancer diagnosis using serum biomarkers, contrary many studies that focus on medical images and demographic data. The primary objective was develop are not only accurate but also provide insights into factors driving predictions, addressing need for trustworthy AI in healthcare. Several classification were evaluated, including OneR, JRIP, FURIA, J48, ADTree, Random Forest, all which known their explainability. dataset included a variety such as electrolytes, metal ions, marker proteins, enzymes, lipid profiles, peptide hormones, steroid hormone receptors. Forest model achieved highest accuracy at 99.401%, followed closely by ADTree 98.802%. OneR J48 98.204% accuracy. Notably, identified oxytocin key predictive biomarker, with most featuring it rules. Other significant parameters GnRH, β-endorphin, vasopressin, IRAP, APB, well like iron, cholinesterase, total protein, progesterone, 5-nucleotidase, BMI, considered clinically relevant pathogenesis. discusses roles development, thus underscoring potential enhancing early focusing explainability use biomarkers.The combination both can lead improved detection personalized treatments, emphasizing these methods clinical settings. markers additional research therapeutic targets pathogenesis deep understanding interactions, advancing approaches management.

Language: Английский

Accelerated and Accurate Cervical Cancer Diagnosis Using a Novel Stacking Ensemble Method with Explainable AI DOI Creative Commons

Md Ismail Hossain Siddiqui,

Shakil Khan,

Zishad Hossain Limon

et al.

Informatics in Medicine Unlocked, Journal Year: 2025, Volume and Issue: unknown, P. 101657 - 101657

Published: May 1, 2025

Language: Английский

Citations

0
Efficient Generative-Adversarial U-Net for Multi-Organ Medical Image Segmentation DOI Creative Commons
Haoran Wang,

Gengshen Wu,

Yi Liu

et al.

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(1), P. 19 - 19

Published: Jan. 12, 2025

Manual labeling of lesions in medical image analysis presents a significant challenge due to its labor-intensive and inefficient nature, which ultimately strains essential resources impedes the advancement computer-aided diagnosis. This paper introduces novel image-segmentation framework named Efficient Generative-Adversarial U-Net (EGAUNet), designed facilitate rapid accurate multi-organ labeling. To enhance model’s capability comprehend spatial information, we propose Global Spatial-Channel Attention Mechanism (GSCA). mechanism enables model concentrate more effectively on regions interest. Additionally, have integrated Mapping Convolutional Blocks (EMCB) into feature-learning process, allowing for extraction multi-scale information adjustment feature map channels through optimized weight values. Moreover, proposed progressively enhances performance by utilizing generative-adversarial learning strategy, contributes improvements segmentation accuracy. Consequently, EGAUNet demonstrates exemplary public datasets while maintaining high efficiency. For instance, evaluations CHAOS T2SPIR dataset, achieves approximately 2% higher Jaccard metric, 1% Dice nearly 3% precision metric comparison advanced networks such as Swin-Unet TransUnet.

Language: Английский

Citations

0
Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer DOI Creative Commons

José Manuel Martínez-Ramírez,

C. J. Carmona, Marı́a Jesús Ramı́rez-Expósito

et al.

Life, Journal Year: 2025, Volume and Issue: 15(2), P. 211 - 211

Published: Jan. 31, 2025

This study explored the application of explainable machine learning models to enhance breast cancer diagnosis using serum biomarkers, contrary many studies that focus on medical images and demographic data. The primary objective was develop are not only accurate but also provide insights into factors driving predictions, addressing need for trustworthy AI in healthcare. Several classification were evaluated, including OneR, JRIP, FURIA, J48, ADTree, Random Forest, all which known their explainability. dataset included a variety such as electrolytes, metal ions, marker proteins, enzymes, lipid profiles, peptide hormones, steroid hormone receptors. Forest model achieved highest accuracy at 99.401%, followed closely by ADTree 98.802%. OneR J48 98.204% accuracy. Notably, identified oxytocin key predictive biomarker, with most featuring it rules. Other significant parameters GnRH, β-endorphin, vasopressin, IRAP, APB, well like iron, cholinesterase, total protein, progesterone, 5-nucleotidase, BMI, considered clinically relevant pathogenesis. discusses roles development, thus underscoring potential enhancing early focusing explainability use biomarkers.The combination both can lead improved detection personalized treatments, emphasizing these methods clinical settings. markers additional research therapeutic targets pathogenesis deep understanding interactions, advancing approaches management.

Language: Английский

Citations

0