Griffith Research Online

Background: Chronic heart failure (CHF) is a serious threat to human health, with high morbidity and mortality rates, imposing a heavy burden on the health care system and society. With the abundance of medical data and the rapid development of machine learning (ML) technologies, new opportunities are provided for in-depth investigation of the mechanisms of CHF and the construction of predictive models. The introduction of health ecology research methodology enables a comprehensive dissection of CHF risk factors from a wider range of environmental, social, and individual factors. This not only helps to identify high-risk groups at an early stage but also provides a scientific basis for the development of precise prevention and intervention strategies.

Objective: This study aims to use ML to construct a predictive model of the risk of occurrence of CHF and analyze the risk of CHF from a health ecology perspective.

Methods: This study sourced data from the Jackson Heart Study database. Stringent data preprocessing procedures were implemented, which included meticulous management of missing values and the standardization of data. Principal component analysis and random forest (RF) were used as feature selection techniques. Subsequently, several ML models, namely decision tree, RF, extreme gradient boosting, adaptive boosting (AdaBoost), support vector machine, naive Bayes model, multilayer perceptron, and bootstrap forest, were constructed, and their performance was evaluated. The effectiveness of the models was validated through internal validation using a 10-fold cross-validation approach on the training and validation sets. In addition, the performance metrics of each model, including accuracy, precision, sensitivity, F1-score, and area under the curve (AUC), were compared. After selecting the best model, we used hyperparameter optimization to construct a better model.

Results: RF-selected features (21 in total) had an average root mean square error of 0.30, outperforming principal component analysis. Synthetic Minority Oversampling Technique and Edited Nearest Neighbors showed better accuracy in data balancing. The AdaBoost model was most effective with an AUC of 0.86, accuracy of 75.30%, precision of 0.86, sensitivity of 0.69, and F1-score of 0.76. Validation on the training and validation sets through 10-fold cross-validation gave an AUC of 0.97, an accuracy of 91.27%, a precision of 0.94, a sensitivity of 0.92, and an F1-score of 0.94. After random search processing, the accuracy and AUC of AdaBoost improved. Its accuracy was 77.68% and its AUC was 0.86.

Conclusions: This study offered insights into CHF risk prediction. Future research should focus on prospective studies, diverse data, advanced techniques, longitudinal studies, and exploring factor interactions for better CHF prevention and management.

Paediatric periorbital cellulitis is a common presentation to both general practitioners and emergency departments. Its management continues to present several challenges and opportunities for improvement.

Climate change is having a profound impact on the Western Pacific region, particularly on Pacific Islands, as highlighted by the United Nations Intergovernmental Panel on Climate Change.1 Impacts include rising temperatures, sea-level rise, an increase in the frequency and intensity of extreme weather events (EWE), and a rise in air pollution, posing severe challenges to the region's ecosystems and the health and well-being of communities.1,2 Climate change has direct and indirect impacts on physical and mental health, exacerbating existing health issues and creating new challenges for healthcare systems, including increased risk of heat-related illnesses, vector-borne diseases, and mental health conditions.2 Climate change has been also linked to suicidal ideation and behaviour.2 In this commentary, we briefly address our current knowledge on climate change and suicidality.

Around 4500 Ross River virus (RRV) human cases are reported in Australia annually. To date, there is no registered nor licenced vaccine to protect against RRV disease. Identifying and substituting preferred with less-preferred codons and dinucleotides is a recognised strategy to attenuate viruses and may prove useful to vaccine development efforts for RRV and other related viruses. Here, we used bioinformatic approaches aimed at assessing evidence of codon usage and dinucleotide bias in 55 RRV whole genomes sampled from humans (Homo sapiens), macropods (Notomacropus agilis), and the Aedes vigilax mosquito. Our results indicate that RRV undergoes positive and negative codon usage bias with natural selection as the major force driving RRV codon usage patterns. RRV displays a bias towards codons with an A or C at the 3rd position while H. sapiens displays a G or C and N. agilis and Ae. vigilax both show bias towards codons with an A or U at the same 3rd position. RRVs codon usage patterns are coincidental to those displayed by common vertebrate hosts and antagonistic to patterns of Ae. vigilax. The coincidental bias identified suggests vertebrate host gene expression greatly influences RRV evolution. In addition, we show that the UG dinucleotides in RRV are overrepresented at all three codon sites, while CA dinucleotides are only overrepresented at codon sites 1–2 and 2–3. These over and under-representations can be exploited to develop attenuated RRV RNA vaccines. The approach utilised here could also be used to develop vaccines for other alphaviruses of global importance.

This study addresses the pressing need to reassess competitiveness within destination contexts and its impact on destination performance and choice, especially amid the ongoing tourism recovery and transformation. Departing from traditional frameworks that often lack empirical grounding or focus predominantly on the supply side, this research adopts a demand-side perspective, centering on the perceptions of Chinese outbound tourists toward destinations in the Asia–Pacific region. Data from a comprehensive online survey, which garnered responses from 1034 Chinese tourists aged 18 or above, possessing passports and ensuring gender-even representation, are understood and analyzed through exploratory factor analysis (EFA) and structural equation modeling with partial least squares (SEM-PLS). The current study not only develops and empirically validates a higher-order composite scale incorporating both reflective and formative constructs but also examines the performance of top-selected destinations in terms of competitiveness across these constructs using ANOVA and post hoc analysis. It aims to offer specific implications for strengthening and revitalizing the tourism industry in Asia–Pacific destinations to ensure vibrancy, inclusivity, and resilience.