What is it about?
Background: Breast cancer is the cancer with the highest mortality and morbidity rate in the world, and its prevention and treatment have become a challenge for the medical community. Machine learning and artificial intelligence methods have been extensively used to forecast breast cancer. Objective: The primary aim of this study is to evaluate and compare the efficacy of 4 conventional machine learning models in the context of breast cancer prediction. The research made use of the Wisconsin Breast Cancer dataset. Methods: To predict breast cancer in this study, 4 conventional machine learning algorithms were employed. Metrics like as ac- curacy, AUC, precision, recall, and F1 score are used to assess the algorithms’ performance. Results: In this study, the accuracy of the Logistic Regression and Support Vector Classifier is 93%. The Full-Data Accuracy of the K-fold cross-validation of Random Forest Classifier and Decision Tree Classifier is 100%.
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Why is it important?
Conclusions: The experimental results in this paper indicate that Logistic Regression and Support Vector Classifier in the Hold-out method have bet- ter performance in predicting breast cancer incidence. problem with better performance. Random Forest Classifier and Decision Tree Classifier have better prediction performance in K-fold cross- validation. This paper provides guiding suggestions for relevant medical practices.
Perspectives
Any region of the body, including the lung, breast, colon, stomach, liver, and brain, can develop cancer, which is a malignant tumor. Based on the latest data presented by the International Agency for Research on Cancer (IARC), it is projected that there will be around 19.3 million newly diagnosed cases of cancer and an estimated 10 million fatalities attributed to cancer worldwide in the year 2020. Based on the findings of the IARC, it has been determined that female breast cancer has surpassed lung cancer in terms of prevalence, emerging as the most often diagnosed form of cancer worldwide. Specifically, female breast cancer accounts for around 11.7% of all newly reported cancer cases.
Yichen Zhang
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This page is a summary of: Construction and study of breast cancer prediction model based on machine learning, October 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3644116.3644202.
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