The Rise in Breast Cancer Diagnosis in Sub-Saharan Africa in the Anti-Retroviral Therapy Era

2021 ◽  
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Sub Saharan Africa ◽  
Sub Saharan

scholarly journals Dissecting the journey to breast cancer diagnosis in sub‐Saharan Africa: Findings from the multicountry ABC‐DO cohort study

2020 ◽  
Vol 148 (2) ◽  
pp. 340-351 ◽  
Author(s):  
Milena Foerster ◽  
Fiona McKenzie ◽  
Annelle Zietsman ◽  
Moses Galukande ◽  
Angelica Anele ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cohort Study ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Sub Saharan Africa ◽  
Sub Saharan

scholarly journals Random Forest Classifier for Diagnosis of Breast Cancer in African Women

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Babafemi Macaulay
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Early Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Sub Saharan Africa ◽  
University Teaching ◽  
African Women ◽  
Breast Cancer Patients ◽  
Health Seeking

Introduction: Breast cancer is the highest cause of cancer-related mortality among women globally. It is documented that 15% of all female cancer is breast cancer. Diagnosis and treatment of breast cancer in its earliest stage remains the only way to improve its outcome and reduce mortality, thus early and accurate diagnosis of breast cancer is important. Early detection of breast cancer among women in Sub-Saharan Africa (SSA) is very challenging to say the least as factors such as low knowledge of breast cancer, lack of awareness of early detection treatment, treatment cost, poor perception of breast cancer, socio-cultural factors such as belief, traditions and fears affect health seeking behaviour of African women but there is limited research efforts in computational approach to diagnosis of breast cancer in SSA. Aim: Here, we propose a novel diagnosis model for African women using Random Forest (RF) machine learning technique. Methods: Study data comprised of technical indicators for 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 (PCA) 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%, and specificity of 100.00% and Area under curve (AUC) 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.

Physical Activity and Survival Subsequent to Metastatic Breast Cancer Diagnosis

2010 ◽  
Author(s):  
Susan Sharp ◽  
Ashleigh Golden ◽  
Cheryl Koopman ◽  
Eric Neri ◽  
David Spiegel
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Metastatic Breast Cancer ◽  
Cancer Diagnosis ◽  
Metastatic Breast ◽  
Breast Cancer Diagnosis

The significance of patient’s knowledge and behavior after breast cancer diagnosis

2019 ◽  
Vol 3 (48) ◽  
pp. 7
Author(s):  
Alina Oana Rusu-Moldovan ◽  
Maria Iuliana Gruia ◽  
Dan Mihu
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
And Behavior
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