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Self-Sponsored Abstract: Introduction: Early detection of breast cancer among women in SubSaharan Africa (SSA) is very challenging because of many factors e.g. low knowledge of breast cancer, lack of awareness of early detection treatment, treatment cost and poor perception of breast cancer. There is dearth of research efforts on computational approach to breast cancer diagnosis in SSA as against what obtains in developed countries. Aim: Here, we propose a novel diagnosis model for African women using a computational technique called Random Forest (RF), a machine learning technique. Materials and Methods: Study data comprised of technical indicators (diagnostic parameters) of breast cancer diagnosis, collected from breast cancer patients attending oncology clinic in Lagos State University Teaching Hospital. A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Nine diagnostic parameters were included. These are clump thickness, marginal adhesion, uniformity of cell size, uniformity of cell shape, single epithelial cell, bare nuclei, bland chromatin, normal nucleoli and mitosis. Principal Component Analysis was used for feature selection and RF model was used for classification. Results: The RF model gave an accuracy of 98.23%, sensitivity of 95.24%, specificity of 100.00% and Area under curve of 98%. Conclusion: The proposed Random Forest model has a good potential at classifying breast cancer in African women. Adoption of computational diagnosis approach in SSA can lead to early diagnosis and reduction of mortality rate.

Random Forest Classifier for Diagnosis of Breast Cancer in African Women