Methods: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.
Results: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.
Conclusions: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.
MATERIALS AND METHODS: This hospital-based, case-control study involved randomly selected 176 pregnant and non-pregnant women attending the University of Abuja Teaching Hospital (UATH), Gwagwalada, Nigeria. Hemoglobin and hematocrit measurements were used to determine anemia incidence, while plasma protein, zinc levels and body mass index (BMI) were used to determine energy index status. Complete blood counts were analyzed using 5 parts-automatic hemo-analyzer, while plasma protein and zinc were analyzed using calorimetric method. Anemia and protein-energy malnutrition were defined using the World Health Organization (WHO) cut-off values.
RESULTS: The mean age of participants was 28.75 ± 5.22 years. Out of 176 participants, 7 (4%) were malnourished while 25% of the participants were anemic. Anemia was significantly associated with participants' occupation (p = 0.002), parity (p<0.001) and gestational age (p<0.001). Most hematological indices, plasma globulin, albumin, protein, and zinc levels were significantly different (p<0.001) among non-pregnant and pregnant women of the first, second and third trimesters.
CONCLUSION: The incidence of anemia and malnutrition was high among study participants. There is a need for improved nutritional intervention, increased awareness and strengthening of health systems in the area of maternal health in Nigeria.