One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate. In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique. A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model. We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Chi-Square selected features. Random Forest Classifier has a good potential at predicting the risk of breast cancer in African women. The study helped to identify the risk factors of breast cancer in African women. This is a valuable information which can help African women to pay attention to those risk factors with the intention of reducing the incidence of breast cancer in Africa.

Coronavirus Disease 2019 (COVID-19) spreads rapidly and is easily contracted by individuals who come near infected persons. With this nature and rapid spread of the contagion, different types of research have been conducted to investigate how non-pharmaceutical interventions can be employed to contain and prevent COVID-19. In this review, we analyzed the key elements of digital contact tracing strategies developed for the prevention and containment of the dreaded epidemic since its outbreak. We carried out a scoping review through relevant studies indexed in three databases, namely Google Scholar, PubMed, and ACM Digital Library. Using some carefully defined search terms, a total of 768 articles were identified. The review shows that 86.32% (n = 101) of the works focusing on contact tracing were published in 2020, suggesting there was an increased awareness that year, increased research efforts, and the fact that the pandemic was given a very high priority by most journals. We observed that many (47.86%, n = 56) of the studies were focused on design and implementation issues in the development of COVID-19 contact tracing systems. In addition, has been established that most of the studies were conducted in 41 countries and that contract tracing app development are characterized by some sensitive issues, including privacy-preserving and case-based referral characteristics.

Background: Using tailored mobile health interventions to improve global vascular risk awareness and control is yet to be investigated for primary stroke prevention in Africa. Methods: This 2-arm pilot randomized controlled trial involved 100 stroke-free adults with at least 2 vascular risk factors for stroke. Eligible participants were assigned randomly to a control arm offering 1-time counseling (n=50) or a 2-month educational intervention arm (n=50) comprising a stroke video and riskometer app aimed at improving stroke risk factor awareness and health-seeking behavioral modification to control total vascular risk. Reduction in total stroke risk score was the primary outcome while feasibility and process measures were secondary outcomes. Results: All enrolled participants completed the 2-month follow-up (retention rate=100%). The mean (SD) age of participants was 59.5 (±12.5) years, 38% were males. The mean change in stroke risk score at 2 months was −11.9% (±14.2) in the intervention arm versus −1.2% (±9.1) in the control arm, P =0.0001. Stroke risk awareness improved by 16.1% (±24.7) in the intervention arm versus 8.9% (±24.7) in the control arm, P =0.08. The intervention arm had 11.1 mm Hg reduction in systolic blood pressure compared with 4.8 mm Hg reduction in the control arm. Conclusions: The intervention demonstrated a positive signal of effect over a 2-month period. A definitive clinical trial with a longer duration of follow-up is warranted on the premise of these promising findings from this pilot randomized clinical trial. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05619406.

It is unclear whether atlas-based parcellation is suitable in aging cohorts because age-related brain changes confound the performance of automatic methods. We assessed atlas-based parcellation of the prefrontal lobe in an aging population using visual assessment and volumetric and spatial concordance. We used an atlas-based approach to parcellate brain MR images of 90 non-demented healthy adults, aged 72.7 ± 0.7 years, and assessed performance. Volumetric assessment showed that both single-atlas- and multi-atlas-based methods performed acceptably (intraclass correlation coefficient [ICC], 0.74-0.76). Spatial overlap measurements showed that multi-atlas (dice coefficient [DC], 0.84) offered an improvement over the single-atlas (DC, 0.75-0.78) approach. Visual assessment also showed that multi-atlas outperformed single atlas and identified an additional postprocessing step of cerebrospinal fluid removal, enhancing concordance (intraclass correlation coefficient, 0.86; DC, 0.89). Atlas-based parcellation performed reasonably well in the aging population. Rigorous performance assessment aided method refinement and emphasizes the importance of age matching and postprocessing. Further work is required in more varied subjects.