While analyzing health data is important for improving health outcomes, class imbalance in datasets poses major challenges to machine learning classification models. This work, therefore, considers the class imbalance problem in stroke prediction using models such as K‐nearest neighbors, support vector machine, logistic regression, random forest, and decision tree. This work balances the stroke dataset, thereby enhancing model performance, through various oversampling strategies: random oversampling (RO), ADASYN, SMOTE, and SMOTE–Tomek. Compared to the results of the imbalanced dataset, all applied oversampling techniques enhanced the correct classification of stroke events by the ML model. Among these, RO–SVM with RBF kernel was the best in terms of sensitivity, specificity, G‐mean, F1‐score, and accuracy values, offering the highest results with respective values of 89.87%, 94.91%, 92.36%, 89.64%, and 89.87%. After applying oversampling techniques, all the machine learning classifications were good enough to classify stroke status, especially for the minority class. This study has highlighted the importance of class imbalance issues in health datasets. Precise detection of instances of minority classes can be enhanced considerably by employing classification models with the implementation of hybrid strategies to effectively solve class imbalance issues, which, in turn, will help improve healthcare outcomes. Further research in integrating more advanced deep learning techniques into other health datasets with imbalances is encouraged to further validate or refine class imbalance approaches, as effective handling of imbalanced classes can substantially promote predictive model performance in the analysis of healthcare.

Introduction: The fundamental transformation of food systems and retail environments in low-income countries is influencing consumers' food choices and dietary habits in unfavourable directions through the consumption of highly processed, energy-dense foods, predominantly manufactured by multinational food corporations. This study aims to identify the principal factors driving consumers' preference for multinational foods over local foods in the urban Accra region of Ghana. Method: This cross-sectional survey involving a random sample of 200 consumers conducted in March/April 2023 using interviewer-administered questionnaires employed a maximum difference scaling approach to investigate the drivers of urban Ghanaian consumer food choices for multinational food corporations' products over local foods. The maximum difference scaling modelling analysis utilized in this study identifies the primary drivers of multinational food corporations' product preferences and the associated trade-offs. Result: The study discovered that food quality and safe packaging, perceived healthiness, taste and flavour, and nutritional value were the most significant factors driving consumer preference for multinational food corporations' products over local foods in Ghana. The criterion food quality and safe packaging had the significantly highest utility than all other attributes in terms of consumer preference for products/meals from multinational food corporations over local foods. Conclusion: The results of this study provide significant contributions to the existing body of research, as previous studies have not identified these factors as primary drivers of multinational food products. Public health authorities and nutritionists can use the study's findings to implement targeted quality assurance measures in local markets and to address the drivers in health education campaigns.

Introduction: Antimicrobials are consumed among patients globally, but in developing and middle-income countries, these drugs can be obtained without a prescription from pharmacies and licensed drug stores due to inadequate regulation in the pharmaceutical sector. This study aimed to assess antimicrobial dispensing practices in medicine sales outlets (i.e., pharmacies and licensed drug stores) to provide quantitative evidence for policy discussions to enhance patient safety and care quality in Ghana’s pharmaceutical industry. Method: The data for this study were obtained from a cross-sectional survey conducted in the Greater Accra region between July and August 2022. The survey was conducted through interviewer-administered questionnaires, and 200 staff members from medicine sales outlets were randomly selected using a two-stage cluster and random sampling technique. The maximum difference experiment model, rooted in random utility theory, was used to analyze their antimicrobial dispensing practices. Result: We found that medicine sales outlet staffs were highly concerned about following the drug act and not dispensing antimicrobials without a prescription, and usually refer a patient to get a prescription from a doctor if the patient has complications (like high fever, generalized malaise, fatigue as symptoms, sinusitis). Stronger concerns were also observed for medicine outlet staff not dispensing antimicrobials without a prescription if the patient is pediatric or geriatric with a severe infection. They also evaluated patients and dispensed antimicrobials based on symptoms, not their age or gender. However, they tended not to dispense antibiotics if the patient had a mild fever and requested it without a prescription. Conclusion: Our results provide insight into the need for a national surveillance system for monitoring antimicrobial prescribing and dispensing practices at medicine sales outlets. Therefore, the selection of antimicrobials for treating infectious diseases may be informed by evidence-based antimicrobial prescribing and dispensing surveillance data and will help policymakers to know the pattern of commonly consumed antimicrobials in the medicine sales outlets.

Introduction: Retinal diseases, a significant global health concern, often lead to severe vision impairment and blindness, resulting in substantial functional and social limitations. This study explored a novel goal of developing and comparing the performance of multiple state-of-the-art convolutional neural network (CNN) models for the automated detection and classification of retinal diseases using optical coherence tomography (OCT) images. Method: The study utilized several models, including DenseNet121, ResNet50, Inception V3, MobileNet, and OCT images obtained from the WATBORG Eye Clinic, to detect and classify multiple retinal diseases such as glaucoma, macular edema, posterior vitreous detachment (PVD), and normal eye cases. The preprocessing techniques employed included data augmentation, resizing, and one-hot encoding. We also used the Gaussian Process-based Bayesian Optimization (GPBBO) approach to fine-tune the hyperparameters. Model performance was evaluated using the F1-Score, precision, recall, and area under the curve (AUC). Result: All the CNN models evaluated in this study demonstrated a strong capability to detect and classify various retinal diseases with high accuracy. MobileNet achieved the highest accuracy at 96% and AUC of 0.975, closely followed by DenseNet121, which had 95% accuracy and an AUC of 0.963. Inception V3 and ResNet50, while not as high in accuracy, showed potential in specific contexts, with 83% and 79% accuracy, respectively. Conclusion: These results underscore the potential of advanced CNN models for diagnosing retinal diseases. With the exception of ResNet50, the other CNN models displayed accuracy levels that are comparable to other state-of-the-art deep learning models. Notably, MobileNet and DenseNet121 showed considerable promise for use in clinical settings, enabling healthcare practitioners to make rapid and accurate diagnoses of retinal diseases. Future research should focus on expanding datasets, integrating multi-modal data, exploring hybrid models, and validating these models in clinical environments to further enhance their performance and real-world applicability.

Background: Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000–138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths and disabilities related to snakebite envenoming by 2030. However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. This study aimed to combine established methodologies with the latest tools in Artificial Intelligence to assess the barriers to effective snakebite treatment in Ghana. Method: We used a MaxDiff statistical experiment design to collect data, and six supervised machine learning models were applied to predict responses whose performance showed an advantage over the other through 6921 data points partitioned using the hold-back validation method, with 70% training and 30% validation. The results were compared using key metrics: Akaike Information Criterion corrected, Bayesian Information Criterion, Root Average Squared Error, and Fit Time in milliseconds. Results: Considering all the responses, none of the six machine learning algorithms proved superior, but the Generalized Regression Model (Ridge) performed consistently better among the candidate models. The model consistently predicted several key significant barriers to effective snakebite treatment, such as the high cost of antivenoms, increased use of unorthodox, harmful practices, lack of access to effective antivenoms in remote areas when needed, and resorting to unorthodox and harmful practices in addition to hospital treatment. Conclusion: The combination of a MaxDiff statistical experiment design to collect data and six machine learning models allowed the identification of barriers to accessing effective therapies for snakebite envenoming in Ghana. Addressing these barriers through targeted policy interventions, including intensified advocacy, continuous education, community engagement, healthcare worker training, and strategic investments, can enhance the effectiveness of snakebite treatment, ultimately benefiting snakebite victims and reducing the burden of snakebite envenoming. There is a need for robust regulatory frameworks and increased antivenom production to address these barriers.