A Comparative Study of Supervised Learning Models for Breast Cancer Tissue Classification
DOI:
https://doi.org/10.48047/1wx2yf51Keywords:
machine learning, breast cancer, healthcare,Abstract
breast cancer issues in women’s life is increasing in the last few years leading to deaths. Machine learning technology helps to increase the chance to improve the quality of treatment and increase the process of making better plans to cure diseases via predicting complex clinical data. This paper presents early-stage breast cancer prediction using supervised learning from medical
text data. Including real-time patient records to verify the prediction can improve the compatibility of models. This study found that SVM provides better accuracy than other supervised models such as LR, KNN, NB, XGB, SVM, RF, and GB. This accuracy can be improved using advanced and robust data analysis methods and feature engineering.
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References
Alladio, E., Trapani, F., Castellino, L., Massano, M., Di Corcia, D., Salomone, A., Berrino, E., Ponzone, R., Marchiò, C., Sapino, A., & Vincenti, M. (2024). Enhancing breast cancer screening with urinary biomarkers and Random Forest supervised classification: A comprehensive investigation.
Journal of Pharmaceutical and Biomedical Analysis, 244, 116113. https://doi.org/10.1016/j.jpba.2024.116113
Pandey, S., Sharma, A., Siddiqui, M. K., Singla, D., & Vanderpuye-Orgle, J. (2020). AI3 PREDICTION OF BREAST CANCER USING K-NEAREST NEIGHBOUR: A SUPERVISED MACHINE LEARNING ALGORITHM. Value in Health, 23, S1. https://doi.org/10.1016/j.jval.2020.04.006
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