The declining fertility rate and increasing marriage age among girls pose challenges for policymakers, leading to issues such as population decline, higher social and economic costs, and reduced labor productivity. Using machine learning (ML) techniques to predict the desire to have children can offer a promising solution to address these challenges. Therefore, this study aimed to predict the childbearing tendency in women on the verge of marriage using ML techniques. Data from 252 participants (203 expressing a \"desire to have children\" and 49 indicating \"reluctance to have children\") in Abadan, and Khorramshahr cities (Khuzestan Province, Iran) was analyzed. Seven ML algorithms, including multilayer perceptron (MLP), support vector machine (SVM), logistic regression (LR), random forest (RF), J48 decision tree, Naive Bayes (NB), and K-nearest neighbors (KNN), were employed. The performance of these algorithms was assessed using metrics derived from the confusion matrix. The RF algorithm showed superior performance, with the highest sensitivity (99.5%), specificity (95.6%), and receiver operating characteristic curve (90.1%) values. Meanwhile, MLP emerged as the top-performing algorithm, showcasing the best overall performance in accuracy (77.75%) and precision (81.8%) compared to other algorithms. Factors such as age of marriage, place of residence, and strength of the family center with the birth of a child were the most effective predictors of a woman\'s desire to have children. Conversely, the number of daughters, the wife\'s ethnicity, and the spouse\'s ownership of assets such as cars and houses were among the least important factors in predicting this desire. ML algorithms exhibit excellent predictive capabilities for childbearing tendencies in women on the verge of marriage, highlighting their remarkable effectiveness. This capacity to offer accurate prognoses holds significant promise for advancing research in this field.

Accurate inflow forecasting is an essential non-engineering strategy to guarantee flood management and boost the effectiveness of the water supply. As inflow is the primary reservoir input, precise inflow forecasting may also offer appropriate reservoir design and management assistance. This study aims to generalize the machine learning model using the support vector machine (SVM), which is support vector regression (SVR), to predict the discharges of the Euphrates River upstream of the Haditha Dam reservoir in Anbar province West of Iraq. Time series data were collected for the period (1986-2024) for the river\'s daily, monthly, and seasonal flow. Different kernel functions of SVR were applied in this study. The kernels are linear, Quadratic, and Gaussian (RBF). The results showed that the daily time scale is better than the monthly and seasonal performance. In contrast, the linear kernel outperformed the other SVR kernel with a time delay of one day based on the value of the coefficient of determination (R2 = 0.95) and the root mean square error (RMSE = 53.29) m3/sec for predicting daily river flow. The results showed that the proposed machine learning model performed well in predicting the daily flow of the Euphrates River upstream of the Haditha Dam reservoir; this indicates that the model might effectively forecast flows, which helps improve water resource management and dam operations.

UNASSIGNED: Cardiovascular disease, type 2 diabetes, and stroke are significant global health concerns. However, gaps persist in understanding the impact of these disorders on women of reproductive age in Central Asia. This study aimed to analyze the health policies implemented in Central Asian countries to address the healthcare needs of this demographic and to forecast future trends in prevalence rates.
UNASSIGNED: We forecasted future trends in prevalence rates, years of life lost, years lived with disability, and disability-adjusted life years for cardiovascular disease, type 2 diabetes, and stroke using publicly available data. Two data sources were utilized: health policy documents issued by the governments of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, and Turkmenistan, and data from the Institute for Health Metrics and Evaluation. Forecasting models, including ARIMA, were employed to predict trends until 2030.
UNASSIGNED: The results indicate an anticipated increase in cardiovascular disease prevalence from 1856.55 in 2020 to 2007.07 by 2029 in Kazakhstan, a subtle increase in Kyrgyzstan from 2492.22 to 2558.69 over 10 years, and similar trends in other countries.
UNASSIGNED: The analysis of policy documents revealed a lack of specific focus on addressing cardiovascular disease, stroke, or type 2 diabetes outside the contexts of pregnancy and childbirth. Understanding these trends is crucial for informing targeted health interventions and resource allocation to mitigate the impact of these diseases on women\'s health in Central Asia.

OBJECTIVE: To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods.
METHODS: Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed.
RESULTS: In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models.
CONCLUSIONS: AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

BACKGROUND: Machine learning (ML) is extensively employed for forecasting the outcome of various illnesses. The objective of the study was to develop ML based classifiers using a stacking ensemble strategy to predict the Japanese Orthopedic Association (JOA) recovery rate for patients with degenerative cervical myelopathy (DCM).
METHODS: A total of 672 patients with DCM were included in the study and labeled with JOA recovery rate by 1-year follow-up. All data were collected during 2012-2023 and were randomly divided into training and testing (8:2) sub-datasets. A total of 91 initial ML classifiers were developed, and the top 3 initial classifiers with the best performance were further stacked into an ensemble classifier with a supported vector machine (SVM) classifier. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicted outcome was the JOA recovery rate.
RESULTS: By applying an ensemble learning strategy (e.g., stacking), the accuracy of the ML classifier improved following combining three widely used ML models (e.g., RFE-SVM, EmbeddingLR-LR, and RFE-AdaBoost). Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top 3 initial classifiers varied a lot in predicting JOA recovery rate in DCM patients.
CONCLUSIONS: The ensemble classifiers successfully predict the JOA recovery rate in DCM patients, which showed a high potential for assisting physicians in managing DCM patients and making full use of medical resources.

BACKGROUND: This study examines global trends in acquired immune deficiency syndrome (AIDS) incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2019, focusing on regional disparities in AIDS incidence, mortality, and DALYs across various levels of socio-demographic index (SDI). It also investigates variations in AIDS incidence, mortality, and DALYs across different age groups, and projects specific trends for the next 25 years.
METHODS: Comprehensive data on AIDS from 1990 to 2019 in 204 countries and territories was obtained from a GBD study. This included information on AIDS incidence, mortality, DALYs, and age-standardized rates (ASRs). Projections for AIDS incidence and mortality over the next 25 years were generated using the Bayesian age-period-cohort model.
RESULTS: From 1990 to 2019, the global incidence of HIV cases increased from 1,989,282 to 2,057,710, while the age-standardized incidence rate (ASIR) decreased from 37.59 to 25.24 with an estimated annual percentage change (EAPC) of -2.38. The ASIR exhibited an upward trend in high SDI and high-middle SDI regions, a stable trend in middle SDI regions, and a downward trend in low-middle SDI and low SDI regions. In regions with higher SDI, the ASIR was higher in males than in females, while the opposite was observed in lower SDI regions. Throughout 1990 to 2019, the age-standardized death rate (ASDR) and age-standardized DALY rate remained stable, with EAPCs of 0.24 and 0.08 respectively. Countries with the highest HIV burden affecting women and children under five years of age are primarily situated in lower SDI regions, particularly in sub-Saharan Africa. Projections indicate a significant continued decline in the age-standardized incidence and mortality rates of AIDS over the next 25 years, for both overall and by gender.
CONCLUSIONS: The global ASIR decreased from 1990 to 2019. Higher incidence and death rates were observed in the lower SDI region, indicating a greater susceptibility to AIDS among women and < 15 years old. This underscores the urgent need for increased resources to combat AIDS in this region, with focused attention on protecting women and < 15 years old as priority groups. The AIDS epidemic remained severe in sub-Saharan Africa. Projections for the next 25 years indicate a substantial and ongoing decline in both age-standardized incidence and mortality rates.

BACKGROUND: Chronic kidney disease (CKD) is an important public health problem worldwide; therefore, forecasting CKD mortality rates and death numbers globally is vital for planning CKD prevention programs. This study aimed to characterize the temporal trends in CKD mortality at the international level from 1990 to 2019 and predict CKD mortality rates and numbers until 2030.
METHODS: Data were obtained from the Global Burden of Disease 2019 Study. A joinpoint regression model was used to estimate the average annual percentage change in CKD mortality rates and numbers. Finally, we used a generalized additive model to predict CKD mortality through 2030.
RESULTS: The number of CKD-related deaths worldwide increased from 591.80 thousand in 1990 to 1425.67 thousand in 2019. The CKD age-adjusted mortality rate increased from 15.95 per 100,000 people to 18.35 per 100,000 people during the same period. Between 2020 and 2030, the number of CKD deaths is forecasted to increase further to 1812.85 thousand by 2030. The CKD age-adjusted mortality rate is expected to decrease slightly to 17.76 per 100,000 people (95% credible interval (CrI): 13.84 to 21.68). Globally, it is predicted that in the next decade, the CKD mortality rate will decrease in men, women, all subgroups of disease etiology except glomerulonephritis, people younger than 40 years old, and all groupings of countries based on the sociodemographic index (SDI) except high-middle-SDI countries.
CONCLUSIONS: The CKD mortality rate is predicted to decrease in the next decade. However, more attention should be given to people with glomerulonephritis, people over 40 years old, and people in high- to middle-income countries because the mortality rate due to CKD in these subgroups is expected to increase until 2030.

UNASSIGNED: Leukemia imposes a large healthcare burden both in China and the United States (US). The disease burden differs greatly between the two countries, but related research is limited. We explored the differences in leukemia incidence and mortality between China and the US.
UNASSIGNED: Data on leukemia in China and the US from 1990 to 2021 were collected from the Global Burden of Disease 2021 database. Incidence and mortality were used to estimate the disease burden, and joinpoint regression was performed to compare their secular trends. We used an age-period-cohort model to analyze the effects of age, period, and birth cohort and project future trends in the next 15 years.
UNASSIGNED: In 2021, the age-standardized incidence rate (ASIR) and the age-standardized death rate (ASDR) of leukemia were lower in China than in the US. However, the incidence and mortality of acute lymphoblastic leukemia (ALL) was considerably higher in China. In the past decades, the ASIR showed decreased tendency in the US, while ASIR showed stable in China. The ASDR tended to decrease in both countries from 1990 to 2021. Males have higher rates of incidence and mortality than females in two countries. The age effects showed that children and older individuals have higher RRs for incidence and mortality in China, while the RRs for incidence and mortality in the US particularly increased in the older population. The disease burden of leukemia in children is obviously greater in China. The ASIRs and ASDRs of leukemia will continue to decline in the next 15 years in China and the US, with the US experiencing a more obvious downtrend.
UNASSIGNED: Over the past decades, the ASDRs in two countries both tended to decrease. And compared to the US, China had lower leukemia incidence and mortality, However, the ASIRs in China tended toward stable, which it was showed downtrend in the US. Children have obviously greater RRs for incidence and mortality in China. The incidence and mortality will decrease continuously in two countries. Effective intervention measures are needed to reduce the burden of leukemia.

BACKGROUND: The number of people with palliative care needs is projected to increase globally. Chile has recently introduced legislation for universal access to palliative care services for patients with severe and terminal illnesses, including non-cancer conditions. We aimed to estimate the number of people affected by serious health-related suffering and need for palliative care in Chile to 2050.
METHODS: We used data on all deaths registered in Chile between 1997-2019 and population estimates for 1997-2050. We used Poisson regression to model past trends in causes of death adjusted by age, sex and population estimates, to project the number of deaths for each cause from 2021 to 2050. We applied the Lancet Commission on Palliative Care and Pain Relief weights to these projections to identify decedents and non-decedents with palliative care needs.
RESULTS: Population palliative care needs in Chile are projected to increase from 117 (95% CI 114 to 120) thousand people in 2021 to 209 (95% CI 198 to 223) thousand people in 2050, a 79% increase (IRR 1.79; 95% CI 1.78-1.80). This increase will be driven by non-cancer conditions, particularly dementia (IRR 2.9, 95% CI 2.85-2.95) and cardiovascular conditions (IRR 1.86, 95% CI 1.83-1.89). By 2050, 50% of those estimated to need palliative care will be non-decedents (not expected to die within a year).
CONCLUSIONS: Chile will experience a large increase in palliative care needs, particularly for people with dementia and other non-cancer conditions. Improved availability of high-quality services, expanded clinician training and new sustainable models of care are urgently required to ensure universal access to palliative care.

Predicting future climate requires the integration of knowledge and expertise from a wide range of disciplines. Predictions must account for climate-change mitigation policies which may depend on climate predictions. This interdependency, or \"circularity\", means that climate predictions must be conditioned on emissions of greenhouse gases (GHGs). Long-range forecasts also suffer from information asymmetry because users cannot use track records to judge the skill of providers. The problems of aggregation, circularity, and information asymmetry can be addressed using prediction markets with joint-outcome spaces, allowing simultaneous forecasts of GHG concentrations and temperature. The viability of prediction markets with highly granular, joint-outcome spaces was tested with markets for monthly UK rainfall and temperature. The experiments demonstrate these markets can aggregate the judgments of experts with relevant expertise, and suggest similarly structured markets, with longer horizons, could provide a mechanism to produce credible forecasts of climate-related risks for policy making, planning, and risk disclosure.