2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 9 , Issue 2 , PP: 19-26, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Hamzah A. Alsayadi 1 * , Abdelaziz A. Abdelhamid 2 , El-Sayed M. El-Kenawy 3 , Abdelhameed Ibrahim 4 , Marwa M. Eid 5
Doi: https://doi.org/10.54216/FPA.090202
Breast cancer is one of the deadliest cancers among women worldwide and one of the main causes of mortality for women in the United States. Breast cancer can be detected earlier and with more accuracy, extending life expectancy at a lower cost. To do this, the efficiency and precision of early breast cancer detection can be increased by evaluating the large data that is currently available utilizing technologies like machine learning fusion-based decision support systems. In this paper, we investigate the prediction performance of various regression models and a decision support system based on these models that provided the predicted category along with a prediction confidence measure. The various machine learning (ML) algorithms applied include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. We used the Breast Cancer Wisconsin Dataset from Wisconsin Prognostic Breast Cancer (WPBC) with 569 digitized images of a fine needle aspirate (FNA) of breast mass and 10 real-valued feature information. Among all five machine learning methods, K-Neighbors regressor had the best performance and ensemble using K-Neighbors regressor gave the best accuracy. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.
Breast Cancer , Ensemble model , Machine learning Fusion , Regression model.
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