Holistic diagnosis tool for early detection of breast cancer
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
University of Bahrain
Abstract
Globally, of all cancer diseases, breast cancer is the number one killer in women. The diseases commonly occur in high income countries, but recently there is rapid increase of breast cancer in middle and low income countries in Asia, Latin America and Africa. This is due to increase in life expectancy, increased urbanization and adoption of western cultures. Although, some strategies to reduce the risks of occurrence of breast cancer are being implemented in developed countries, the case in middle and low income countries is that majority of breast cancer patients are affected by the disease due to diagnosis at late stages of the disease. Therefore, early detection of breast cancer is needed to overcome this problem. In this paper, a holistic diagnosis tool for early detection of breast cancer is proposed. The tool is software based utilizing a novel prediction model for breast cancer survivability developed by using available data mining (DM) technologies. Specifically, five popular data mining algorithms (logistic regression, decision tree, support vector machine, K nearest neighbors and random forest) were used to develop the prediction tool using Wisconsin breast cancer data set. In the paper, prediction tool training and test set results are reported. Achieved from the reported work of training sets are classification accuracies of 100%(Decision Tree); 99.8046%(Random Forest); 97.46%(Logistic Regression and Support Vector Machine); 97.07%(K Nearest Neighbors) and for testing sets are classification accuracies of 93.5672%(Decision Tree); 92.9%(Random Forest); 92.39%(Logistic Regression, Support Vector Machine and K Nearest Neighbors). These results are much better than those reported in the literature. The results show that the proposed DM disease prediction tool has potential to greatly impact on current patient management, care and future interventions against the breast cancer disease and through customization even against other deadly diseases.
Description
Abstract. Full text article.Also available at http://dx.doi.org/10.12785/ijcds/100141
Keywords
Breast cancer, Data mining, Machine learning, Data mining algorithms, Breast cancer detection, Breast cancer survivability, Cancer disease
Citation
Diwani, S. A., & Yonah, Z. O. (2021). Holistic Diagnosis Tool for Early Detection of Breast Cancer. International Journal of Computing and Digital Systems, 10(2).