Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective.

BACKGROUND: Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 (MDG5) by 2015 and may struggle to meet Sustainable Development Goal 3 (SDG3) despite maternal healthcare interventions. Concerns arise about Ghana's ability to achieve the World Health Organization's neonatal mortality goal of 12 per 1000 live births by 2030. This study aims to identify key factors influencing childbirth outcomes and create a predictive method for high-risk pregnancies. METHODS: We compared four machine learning classifiers (Extreme Gradient Boosting, Random Forest, Logistic Regression, and Artificial Neural Network) in predicting childbirth outcomes using data from a tertiary health facility in Ghana. To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). RESULTS: Our findings show that fetal heartbeat, gestation age at birth are the most influential factors on birth outcome (stillbirth or live birth), while there is no significant association with maternal age, number of babies, and type of delivery method. Among the machine learning models considered, Random Forest emerged as the optimal model achieving an accuracy, F1-score, and AUC values of approximately 0.98, 0.99, and 0.90 respectively. CONCLUSION: Our study identifies key factors affecting childbirth outcomes and highlights the potential of machine learning for early high-risk pregnancy detection in clinical settings. These findings are crucial for Ghana and other Sub-Saharan African countries striving to meet maternal and neonatal healthcare goals. Further research and policy initiatives can use these results to improve healthcare in the region and work toward the World Health Organization's objectives by 2030.

Assessing the impact of climate variability on maize yields in the different regions of Ghana-A machine learning perspective.

Climate variability has become one of the most pressing issues of our time, affecting various aspects of the environment, including the agriculture sector. This study examines the impact of climate variability on Ghana's maize yield for all agro-ecological zones and administrative regions in Ghana using annual data from 1992 to 2019. The study also employs the stacking ensemble learning model (SELM) in predicting the maize yield in the different regions taking random forest (RF), support vector machine (SVM), gradient boosting (GB), decision tree (DT), and linear regression (LR) as base models. The findings of the study reveal that maize production in the regions of Ghana is inconsistent, with some regions having high variability. All the climate variables considered have positive impact on maize yield, with a lesser variability of temperature in the Guinea savanna zones and a higher temperature variability in the Volta Region. Carbon dioxide (CO2) also plays a significant role in predicting maize yield across all regions of Ghana. Among the machine learning models utilized, the stacking ensemble model consistently performed better in many regions such as in the Western, Upper East, Upper West, and Greater Accra regions. These findings are important in understanding the impact of climate variability on the yield of maize in Ghana, highlighting regional disparities in maize yield in the country, and highlighting the need for advanced techniques for forecasting, which are important for further investigation and interventions for agricultural planning and decision-making on food security in Ghana.

The impacts of global economic policy uncertainty on green bond returns: A systematic literature review.

This study utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to investigate the interconnectedness of green bond with various financial markets, aiming to clarify their relationship with global economic uncertainty and their impact on returns. After a comprehensive search of pertinent research papers from January 2016 to September 2023, 79 relevant articles were identified. The analysis delves into the evolution of research on green bonds' interactions with economic policy uncertainty considering the financial markets, analytical methodologies, contributions to the field, and the role of green bonds under both normal and extreme market conditions. The study reveals noteworthy findings: firstly, the interplay between green bonds and financial markets is influenced by macroeconomic factors, such as the COVID-19 pandemic and the Russia-Ukraine conflict in 2022, which were significant sources of economic policy uncertainty during the study period. Secondly, during times of global economic uncertainties, green Bonds act as net transmitters of spillovers in the short term but shifts to net receivers in the long term, positioning them as strategic hedging assets rather than safe-havens, particularly against spillovers from crude oil and CO2 emission in times of economic uncertainties. Additionally, the review highlights prevalent methodologies employed to assess the relationship between global economic policy uncertainty and green bonds. Some of which include quantile approaches, the Diebold & Yilmaz 2012 spillover index, as well as various models like VAR models, GARCH models, ARDL models. Notably, certain countries like China, the United Kingdom, and Vietnam emerge as key contributors to this research domain. The review not only consolidates existing knowledge but also provides valuable insights for investors and policymakers regarding green bonds in terms of risk management and asset allocation, while also pointing towards potential avenues for future research in this field.