Prediction of breast cancer using regression model

Gongyin Hong

doi:10.1117/12.2660386

6 December 2022 Prediction of breast cancer using regression model

Gongyin Hong

Proceedings Volume 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022); 1245819 (2022) https://doi.org/10.1117/12.2660386
Event: International Conference on Biomedical and Intelligent Systems, 2022, Chengdu, China

Abstract

Breast cancer is the most common malignant tumor nowadays, which is even more common than lung cancer among human being. Fine Needle Aspiration (FNA) is the most common way to detect whether the breast mass is malignant or not. Although the rate of survive after the malignant tumor spreads is pretty low, if the tumor can be noticed at early stage, patients still hold a great chance of survive. With a low death rate in early stage comparing to other cancers, a convenient and efficient way to help diagnosing whether the breast mass is benign or malignant is crucial. However, many existing models on predicting breast cancer includes variables of the patients themselves, like weight changes and lifestyle, which requires long-term tracking survey, and that will cost a lot of precious time during the early stage of the tumor. Thus, a model which only consider the numeric variable from the FNA would save a lot of time and increase the chance of living. In this study a model is created based on the FNA data of the breast tumor patients in Wisconsin in 1995. A set of variable filtrations are employed to get the best variable combination such as AIC, ridge regression, variance inflation factor (VIF), LASSO, and cross validation. After the variable filtration a model based on General Linear Model (GLM) is created with an accuracy of 0.88.

Citation Download Citation

Gongyin Hong "Prediction of breast cancer using regression model", Proc. SPIE 12458, International Conference on Biomedical and Intelligent Systems (IC-BIS 2022), 1245819 (6 December 2022); https://doi.org/10.1117/12.2660386

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KEYWORDS

Data modeling

Fine needle aspiration

Breast cancer

Tumors

Breast

Cancer

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