A systematic literature review of machine learning based risk prediction models for diabetic retinopathy progression.

Diabetic Retinopathy (DR) is the most popular debilitating impairment of diabetes and it progresses symptom-free until a sudden loss of vision occurs. Understanding the progression of DR is a pressing issue in clinical research and practice. In this systematic review of articles on Machine Learning (ML) based risk prediction models for DR progression, ever since the use of Artificial Intelligence (AI) for DR detection, there have been more cross-sectional studies with different algorithms of use of AI, there haven't been many longitudinal studies for the AI based risk prediction models. This paper proposes a novel review to fill in the gaps identified in current reviews and facilitate other researchers with current research solutions for developing AI-based risk prediction models for DR progression and closely related problems; synthesize the current results from these studies and identify research challenges, limitations and gaps to inform the selection of machine learning techniques and predictors to build novel prediction models. Additionally, this paper suggested six (6) deep AI-related technical and critical discussion of the adopted strategies and approaches. The Systematic Literature Review (SLR) methodology was employed to gather relevant studies. We searched IEEE Xplore, PubMed, Springer Link, Google Scholar, and Science Direct electronic databases for papers published from January 2017 to 30th April 2023. Thirteen (13) studies were chosen on the basis of their relevance to the review questions and satisfying the selection criteria. However, findings from the literature review exposed some critical research gaps that need to be addressed in future research to improve on the performance of risk prediction models for DR progression.

Microarray Gene Expression Data Classification via Wilcoxon Sign Rank Sum and Novel Grey Wolf Optimized Ensemble Learning Models.

Cancer is a deadly disease that affects the lives of people all over the world. Finding a few genes relevant to a single cancer disease can lead to effective treatments. The difficulty with microarray datasets is their high dimensionality; they have a large number of features in comparison to the small number of samples in these datasets. Additionally, microarray data typically exhibit significant asymmetry in dimensionality as well as high levels of redundancy and noise. It is widely held that the majority of genes lack informative value about the classes under study. Recent research has attempted to reduce this high dimensionality by employing various feature selection techniques. This paper presents new ensemble feature selection techniques via the Wilcoxon Sign Rank Sum test (WCSRS) and the Fisher's test (F-test). In the first phase of the experiment, data preprocessing was performed; subsequently, feature selection was performed via the WCSRS and F-test in such a way that the (probability values) p-values of the WCRSR and F-test were adopted for cancerous gene identification. The extracted gene set was used to classify cancer patients using ensemble learning models (ELM), random forest (RF), extreme gradient boosting (Xgboost), cat boost, and Adaboost. To boost the performance of the ELM, we optimized the parameters of all the ELMs using the Grey Wolf optimizer (GWO). The experimental analysis was performed on colon cancer, which included 2000 genes from 62 patients (40 malignant and 22 benign). Using a WCSRS test for feature selection, the optimized Xgboost demonstrated 100% accuracy. The optimized cat boost, on the other hand, demonstrated 100% accuracy using the F-test for feature selection. This represents a 15% improvement over previously reported values in the literature.

Prevalence and correlates of psychological distress amongst healthcare workers during the COVID-19 pandemic: An online survey

Background: Understanding the mental health needs of healthcare workers during coronavirus disease 2019 (COVID-19) pandemic with a view of mitigating its impact on occupational functioning is vital. Aim: To determine the prevalence and correlates of psychological distress amongst healthcare workers. Setting: The study was carried out in Nigeria during COVID-19 pandemic. Methods: A cross-sectional quantitative study using a convenience sample was conducted amongst healthcare workers. The survey comprised of two Google formatted self-report questionnaires, a 12-item General Health Questionnaire and a questionnaire containing socio-demographic, work related and knowledge of COVID-19 variables. Questionnaires were distributed via closed professional WhatsApp platforms. Data were analysed using descriptive statistics, chi-square test and logistic regression. Results: There were 313 respondents and prevalence of psychological distress was 47.0%. Females were almost twice as likely to have psychological distress as males (OR = 1.92, 95% CI: 1.21­3.04, p = 0.006). Healthcare workers who had no contact with COVID-19 positive patients had an 87% reduced risk of psychological distress (OR = 0.13, 95%CI: 0.23­0.25, p = 0.018) compared with those who had contact with COVID-19 positive patients. Healthcare workers with poor knowledge of COVID-19 had a 44% reduced risk of psychological distress compared with those with good knowledge (OR = 0.56, 95% CI: 0.34­0.93, p = 0.025). Conclusion: Findings revealed that healthcare workers in Nigeria reported psychological distress during COVID-19 pandemic. Greater risk was amongst females and those who had contact with COVID-19 positive patients whilst poor knowledge was protective.