Breast cancer prediction using machine learning classification algorithms

In bioinformatics, the integration of machine learning has revolutionized disease diagnosis. Machine learning algorithms remove human limitations, offering more accuracy in diagnosing diseases like cancer. Breast cancer, the second most diagnosed cancer in women, often relies on mammography, which i...

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Veröffentlicht in:Intelligence-based medicine Jg. 11; S. 100193
Hauptverfasser: La Moglia, Alan, Mohamad Almustafa, Khaled
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.01.2025
Schlagworte:
Bioinformatics
Breast cancer
Informatics
Machine learning
Machine learning classifiers
Breast cancer
Bioinformatics
Machine learning classifiers
Machine learning
ISSN:2666-5212, 2666-5212
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Zusammenfassung:In bioinformatics, the integration of machine learning has revolutionized disease diagnosis. Machine learning algorithms remove human limitations, offering more accuracy in diagnosing diseases like cancer. Breast cancer, the second most diagnosed cancer in women, often relies on mammography, which is only 70 % accurate, leading to potential misdiagnosis. Biopsies, though more reliable, are subject to human error and conflicting specialist opinions, often requiring multiple biopsies. The shortage of pathologists further complicates accurate and timely diagnoses. Machine learning can reduce these errors, providing faster and more precise results. In this study, a breast cancer dataset with 11 features is analyzed using eight machine learning classifiers. Results showed that Logistic Regression achieved the highest testing accuracy of 91.67 % without feature selection. After applying feature selection, classifiers like LGBM improved, with a notable 90.74 % accuracy. This study highlights the importance of integrating machine learning into healthcare, not only for breast cancer but for other diseases like heart disease and diabetes. Continued exploration and application of machine learning in bioinformatics will enhance its accessibility and effectiveness for medical professionals worldwide, leading to improved patient outcomes.
ISSN:2666-5212
2666-5212
DOI:10.1016/j.ibmed.2024.100193