Predictive Analytics Model for Breast Cancer Prognosis

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Author(s): Shraddha Patel (Amity University, Lucknow, India)and Parul Verma (Amity University, Lucknow, India)
Copyright: 2025
Pages: 18
Source title: Assistive Technology Solutions for Aging Adults and Individuals With Disabilities
Source Author(s)/Editor(s): Rajiv Pandey (Amity University, Lucknow, India), Pratibha Maurya (Amity University, Lucknow, India), Jigna Bhupendra Prajapati (Ganpat University, India), Raju Halder (Indian Institute of Technology, Patna, India)and Kanishka Tyagi (UHV Technologies, USA)
DOI: 10.4018/979-8-3693-6308-9.ch009

Keywords: Assistive Technologies / Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

Breast cancer remains a leading cause of mortality among women globally, necessitating the development of robust prognostic models to enhance patient outcomes and guide clinical decision-making. In this study, Our analysis incorporates critical cell details including Clump Thickness, Uniform Cell Size, Uniform Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli, and Mitoses. They are obtained through microscopic examination of stained breast tissue samples. Pathologists assess these features by evaluating the morphology and structure of cells under a microscope, quantifying each attribute to provide detailed cellular characteristics. The results indicate that machine learning models can provide significant insights into the factors influencing breast cancer prognosis. Among the models evaluated, the Decision Tree Classifier and SVC demonstrated superior performance in terms of accuracy and predictive power.

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Predictive Analytics Model for Breast Cancer Prognosis

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