Minimum Akaike Information Criterion Research Articles

PERBANDINGAN METODE EXPONENTIAL GARCH (EGARCH) DAN GLOSTEN-JAGANNATHAN-RUNKLE GARCH (GJR-GARCH) PADA MODEL VOLATILITAS SAHAM TUNGGAL

Financial data, such as stock prices, usually have a tendency to fluctuate rapidly and create a heteroscedastic effect on the variance of residuals. The Covid-19 pandemic that occurred from 2020 to 2022 is one factor that can affect economic movements, especially in Indonesia, and has an impact on the volatility of financial data. The problem of heteroscedasticity can be addressed using the ARCH/GARCH model. However, this model has a weakness in capturing the asymmetry of volatility resulting from good news and bad news. Several models that can overcome the problem of volatility asymmetry are EGARCH and GJR-GARCH. The purpose of this thesis research is to determine the best volatility model using the daily stock price data of PT Bank Rakyat Indonesia (Persero) Tbk. covering the period from February 2020 to February 2023. The result of the asymmetric GARCH model suggests that the ARIMA(2,0,2) EGARCH(1,1) model has an AIC value of -4.8850, and the ARIMA(2,0,2) GJR-GARCH(1,1) model has an AIC value of -4.8907. Therefore, the model with the minimum AIC value, which is the ARIMA(2,0,2) GJR-GARCH(1,1) model, is considered the best model. Furthermore, this model exhibits very good forecast accuracy, as evaluated by the sMAPE value of 5,17%.

Comparative Performance of ARIMA and ARCH Models On Time Series of Monthly Libyan Brent Oil Price

Many economic and financial time series exhibit a phenomenon known as heteroscedastic, where the variance of the series changes over time. This research study focuses on financial time series modelling, with special application to modelling the price of Libyan Brent Oil. In particular, the theory of univariate nonlinear time series analysis is explored and applied to the price Libyan of Brent Oil, spanning from January 2000 to December 2010. The data was obtained from Bullent of the Platts Market Wire of Statistics. This study aims to evaluate the performance of ARIMA as a linear model and ARCH as a nonlinear modelling data. Multiple time series models were considered for fitting the data, and the best ARIMA models were selected based on the Akaike Information Criteria (AIC). ARIMA (0, 1, 1), and ARIMA (1, 1, 0) were identified as the best models. After estimating the parameters of the selected models, model checking revealed that these models were not suitable for modeling the data, as they lacked validity according to the test of squared residuals. The goodness of fit was assessed using the AIC, and based on minimum AIC values, the best fit ARCH models were found to be ARIMA (0, 1, 1) - ARCH (1). After estimating the parameters of the selected model, a series of diagnostic and forecast accuracy tests were performed. Based on this model, a twelve-month forecast of the price of Libyan Brent crude was made.

Mandibular second molar impaction: introducing a novel and validated 3D classification system

BackgroundMandibular second molar (M2M) impaction is a clinically significant manifestation of eruption disturbance in dental development. The primary aim of this study was to investigate the impact of the three-dimensional (3D) characterization on clinical and therapeutic decisions for M2M impaction. The secondary aim was to introduce a validated 3D classification system incorporating both surgical and orthodontic parameters.MethodsBidimensional (2D) and 3D radiological records of 15 impacted M2M were collected and deidentified. Ten experienced clinicians (5 oral surgeons;5 orthodontists) categorized each case, first based on 2D records and then with 3D scans. The degree of orthodontic and surgical difficulty in treating impacted M2M was evaluated using a novel classification system based on anatomical and radiological features. The primary outcome variable was the assessment of differences in diagnosis and decision-making protocol using 2D or 3D records, where clinical relevance ranged from 0 to 4. The secondary outcome variable was the validation analysis of the proposed 3D classification system to determine the concordance among the clinicians. Descriptive statistics and multivariable inferential analysis based on Akaike information criterion (AIC) were performed (α = 0.05).Results3D examination allowed a better visualization of M2M impaction with higher clinical relevance for diagnosis of M2M root relationship to alveolar nerve and lingual plate, height to alveolar crest, depth, and inclination relative to the first molar and position relative to the third molar (range:2.69–3.43). The proposed 3D classification of M2M impaction changed clinical decisions regarding surgical-orthodontic approach, biomechanics, patient education, and treatment time estimate (range:2.59–3.33). In the validation analysis of the classification, no evidence of inter- or intra-group (surgeon/orthodontist) bias in score attribution occurred: the model with the minimum AIC was the null model (AIC = 718.04).Conclusion3D evaluation of impacted M2Ms could enhance diagnostic accuracy, and a classification system was proposed and validated by a group of experienced surgeons and orthodontists with high concordance.

Modeling an evaluation framework for adding IoT water-level sensors based on ANN-derived 2D inundation simulations

ABSTRACT This study aims to develop a smart model for evaluating the spatial density of added IoT sensors (called AIOT grids) to optimize their amount and placements, named SM_ESD_AIOT model; the proposed SM_ESD_AIOT model mainly collaborates cluster analysis with Akaike information criterion (AIC) based on the resulting 2D inundation simulations from the ANN-derived model in comparison with those from the physically based hydrodynamic (SOBEK) model under various sets of AIOT-based sensor networks. Miaoli City in northern Taiwan is selected as the study with the three practical IoT sensors; also, the 1,939 electrical poles are treated as the potential AIOT grids grouped under 5, 10, 15, and 20 clusters. Using a simulated rainfall-induced flood event of 51 h, the five AIOT-based sets, consisting of five added and three practical IoT sensors, could be selected as the optimal one with the minimum AIC (around 1.45). Also, on average, the 2D inundation simulation indices from the optimal five AIOT-based sensor networks are 0.7 better than the results from the three IoT sensors (about 0.495). As a result, the proposed SM_ESD_AIOT is shown to efficiently optimize the amount and placements of the AIOT sensors to enhance the reliability and accuracy of 2D inundation simulation.

Genetic parameter estimates for growth traits in Texel sheep of Brazil

The present study aimed to estimate the genetic parameters for direct and maternal influences on birth weight (BW), weaning weight (WW) and postweaning weight (PW) of Texel sheep, raised in an extensive system from Brazil. Data file used in this study consisted of 834 body weight records of 834 Texel lambs (376 males and 458 females) born from 2020 to 2021, offspring of 475 ewes and 45 rams. Multivariate animal model was conducted to estimate the variance components, heritability and genetic correlations using the restricted maximum likelihood method in ASReml-R software, while the effects of fixed factors were analyzed by the general linear model procedure of statistical analysis system. Five different animal models, with or without maternal genetic effect, maternal permanent environment effect and covariance between additive genetic and maternal genetic effects, were fitted on the growth traits data of the Texel sheep population. AIC and BIC test results were used to evaluate the best fit models for growth traits. The effect of lamb's sex was significant on BW, WW and PW while birth type and year of lambing effects were on all traits (P < 0.05). Model 4, incorporating direct additive genetic, maternal additive genetic and maternal permanent environmental effects showed the minimum AIC estimate for all growth traits. The direct additive genetic heritability for BW, WW and PW were 0.11 ± 0.03, 0.37 ± 0.05 and 0.31 ± 0.08, respectively and maternal heritability for these traits were 0.00 ± 0.00, 0.04 ± 0.02 and 0.00 ± 0.00, respectively. The total heritability estimates for BW, WW and PW were 0.11 ± 0.03, 0.39 ± 0.05 and 0.31 ± 0.08, respectively. The estimates of the direct additive genetic correlation between BW-WW, BW-PW and WW-PW were 0.68 ± 0.12, 0.66 ± 0.12 and 0.78 ± 0.07, respectively. The maternal permanent environmental correlation between BW-WW, BW-PW and WW-PW were 0.38 ± 0.27, 0.29 ± 0.20 and 0.74 ± 0.16, respectively. Phenotypic correlation ranged from 0.43 ± 0.03 to 0.75 ± 0.02. It is concluded that due to the existence of genetic variation for BB, WW and PW and strong genetic correlation among them, genetic progress for these traits is possible by selection in Texel sheep from Brazil.

How Certain are You of Your Minimum AIC or BIC Values?

How Certain are You of Your Minimum AIC or BIC Values?

Long Memory Time-series Model (ARFIMA) Based Modelling of Jute Prices in the Samsi Market of Malda District, West Bengal

The objective of this paper is modeling and forecasting the weekly jute prices of Samsi market in the Malda district of West Bengal in the presence of long memory process. The long memory behavior of series is investigated by the ACF plot and Hurst R/S analysis. A fractionally integrated autoregressive moving-average (ARssFIMA) model is fitted using 668 weekly data (January 2009-November 2022). This study shows the efficiencies of the Hurst exponent, GPH, Smoothed periodogram, Local Whittle, and Wavelet methods used to estimate the fractional difference parameter in the ARFIMA model. Furthermore, we compared the forecasting abilities of the ARFIMA and ARIMA models. The results show that long memory is present in the jute price series. The models selected according to each method are ARFIMA (3,0.348,0), ARFIMA (3,0.291,1), ARFIMA (2,0.487,0), ARFIMA (3,0.461,0), ARFIMA (2,0.311,0), and ARIMA (2,1,1) on the basis of minimum AIC and BIC using 534 in-sample data. Finally, the wavelet method based ARFIMA (2,0.311,0) model is found to be the best optimal model in terms of MAE, RMSE, and MAPE criteria using 134 out-of-sample data. A comparative study indicates that the forecasting performance of the ARFIMA model is strongly better than that of the ARIMA model in this regard.

Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India).

Air pollution is a significant issue for a developing country like India and the air quality index (AQI) forecasting helps to predict air quality levels in advance and allows individuals to take precautionary measures to protect their health. The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model. Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey-Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values. With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)[12] model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year. The seasonal ARIMA (0,1,0) (1,0,0)[12] model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients' health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance.

A nomogram incorporating treatment data for predicting overall survival in gastroenteropancreatic neuroendocrine tumors: a population-based cohort study.

Over the last few decades, the annual global incidence of gastroenteropancreatic neuroendocrine tumours (GEP-NETs) has steadily increased. Because of the complex and inconsistent treatment of GEP-NETs, the prognosis of patients with GEP-NETs is still difficult to assess. The study aimed to construct and validate the nomograms included treatment data for prediction overall survival (OS) in GEP-NETs patients. GEP-NETs patients determined from the Surveillance, Epidemiology, and End Results (SEER)-13 registry database (1992-2018) and with additional treatment data from the SEER-18 registry database (1975-2016). In order to select independent prognostic factors that contribute significantly to patient survival and can be included in the nomogram, multivariate Cox regression analysis was performed using the minimum value of Akaike information criterion (AIC) and we analyzed the relationship of variables with OS by calculating hazard ratios (HRs) and 95% CIs. In addition, we also comprehensively compared the nomogram using to predict OS with the current 7th American Joint Committee on Cancer (AJCC) staging system. From 2004 to 2015, a total of 42662 patients at diagnosis years with GEP-NETs were determined from the SEER database. The results indicated that the increasing incidence of GEP-NETs per year and the highest incidence is in patients aged 50-54. After removing cases lacking adequate clinicopathologic characteristics, the remaining eligible patients ( n =7564) were randomly divided into training (3782 patients) and testing sets (3782 patients). In the univariate analysis, sex, age, race, tumour location, SEER historic stage, pathology type, TNM, stage, surgery, radiation, chemotherapy, and CS tumour size were found to be significantly related to OS. Ultimately, the key factors for predicting OS were determined, involving sex, age, race, tumour location, SEER historic stage, M, N, grade, surgery, radiation, and chemotherapy. For internal validation, the C-index of the nomogram used to estimate OS in the training set was 0.816 (0.804-0.828). For external validation, the concordance index (C-index) of the nomogram used to predict OS was 0.822 (0.812-0.832). In the training and testing sets, our nomogram produced minimum AIC values and C-index of OS compared with AJCC stage. Decision curve analysis (DCA) indicated that the nomogram was better than the AJCC staging system because more clinical net benefits were obtained within a wider threshold probability range. A nomogram combined treatment data may be better discrimination in predicting overall survival than AJCC staging system. The authors highly recommend to use their nomogram to evaluate individual risks based on different clinical features of GEP-NETs, which can improve the diagnosis and treatment outcomes of GEP-NETs patients and improve their quality of life.

Development and validation of a nomogram to predict intracranial haemorrhage in neonates

BackgroundThe aim of this study was to establish and validate a Susceptibility-weighted imaging (SWI)-based predictive model for neonatal intracranial haemorrhage (ICH). MethodsA total of 1190 neonates suspected of ICH after cranial ultrasound screening in a tertiary hospital were retrospectively enrolled. The neonates were randomly divided into a training cohort and a internal validation cohort by a ratio of 7:3. Univariate analysis was used to analyze the correlation between risk factors and ICH, and the prediction model of neonatal ICH was established by multivariate logistic regression based on minimum Akaike information criterion (AIC). The nomogram was externally validated in another tertiary hospital of 91 neonates. The performance of the nomogram was evaluated in terms of discrimination by the area under the curve (AUC), calibration by the calibration curve and clinical net benefit by the decision curve analysis (DCA). ResultsUnivariate analysis and min AIC-based multivariate logistic regression screened the following variables to establish a predictive model for neonatal ICH: Platelet count (PLT), gestational diabetes, mode of delivery, amniotic fluid contamination, 1-min Apgar score. The AUC was 0.715, 0.711, and 0.700 for the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curve showed a good correlation between the nomogram prediction and actual observation for ICH. DCA showed the nomogram was clinically useful. ConclusionWe developed and validated an easy-to-use nomogram to predict ICH for neonates. This model could support individualized risk assessment and healthcare.

Econometric Modeling and Forecasting of Arabica and Robusta Coffee Production for Sustainable Agriculture Development

In the present study, we have used Box-Jenkins approaches an Autoregressive Integrated Moving Average model (ARIMA) for modeling and forecasting of annual amount of Arabica and Robusta coffee production and yield (ARCPY) in India. In this study used time series data was collected from the official website of the coffee board of India from 1986 to 2023 (38 observations). Augmented Dickey-Fuller (ADF) test has used for testing the stationarity of the time series, and the appropriate ARIMA model has selected based on minimum Akaike Information Criterion (AIC). The ARIMA models has compared with the other ARIMA models with respect to forecast accuracy measures, and the residuals has diagnosed for possible presence of autocorrelation, and white noise heteroscedasticity (WNH) test of the fitted models. The MAPE value of ACP and ACY has 8.94 and 9.35 percent respectively shows highly accurate forecasting percentage rate respectively. While, the MAPE value of RCP and RCY has 16.03 and 11.43 percent respectively shows good accurate forecasting percentage rate respectively. Thus, we found the ARIMA (2, 1, 4), (3, 1, 2), (0, 1, 3) and (2, 0, 1) models for Arabica and Robusta coffee; which has observed as the best suitable model for predicting the future annual amount of Arabica coffee production (ACP), Arabica coffee yield (ACY), Robusta coffee production (RCP) and Robusta coffee yield (RCY) respectively, and we have estimated that the annual amount of ACP and ACY achieved in the year 2023-24 from 97379.67 MTs, and 472.29 kg/hectare respectively to 93272.91 MTs, and 379.31 kg/hectare respectively in the year 2034-35 will decrease, and the annual amount of RCP and RCY achieved in the year 2023-24 from 268655.21 MTs, and 1110.68 kg/hectare respectively to 318614.85 MTs, and 1012.90 kg/hectare respectively in the year 2034-35 will reach.

Trends of hospitalisation among new admission inpatients with oesophagogastric variceal bleeding in cirrhosis from 2014 to 2019 in the Affiliated Hospital of Southwest Medical University: a single-centre time-series analysis

ObjectivesThis study aimed to assess the internal law and time trend of hospitalisation for oesophagogastric variceal bleeding (EGVB) in cirrhosis and develop an effective model to predict the trend of...

A new prognostic model for recurrent pregnancy loss: assessment of thyroid and thromboelastograph parameters.

This study aimed to identify predictors associated with thyroid function and thromboelastograph (TEG) examination parameters and establish a nomogram for predicting the risk of subsequent pregnancy loss in recurrent pregnancy loss (RPL). In this retrospective study, we analyzed the medical records of 575 RPL patients treated at Lanzhou University Second Hospital, China, between September 2020 and December 2022, as a training cohort. We also included 272 RPL patients from Ruian People's Hospital between January 2020 and July 2022 as external validation cohort. Predictors included pre-pregnancy thyroid function and TEG examination parameters. The study outcome was pregnancy loss before 24 weeks of gestation. Variable selection was performed using least absolute shrinkage and selection operator regression and stepwise regression analyses, and the prediction model was developed using multivariable logistic regression. The study evaluated the model's performance using the area under the curve (AUC), calibration curve, and decision curve analysis. Additionally, dynamic and static nomograms were constructed to provide a visual representation of the models. The predictors used to develop the model were body mass index, previous pregnancy losses, triiodothyronine, free thyroxine, thyroid stimulating hormone, lysis at 30 minutes, and estimated percent lysis which were determined by the multivariable logistic regression with the minimum Akaike information criterion of 605.1. The model demonstrated good discrimination with an AUC of 0.767 (95%CI 0.725-0.808), and the Hosmer-Lemeshow test indicated good fitness of the predicting variables with a P value of 0.491. Identically, external validation confirmed that the model exhibited good performance with an AUC of 0.738. Moreover, the clinical decision curve showed a positive net benefit in the prediction model. Meanwhile, the web version we created was easy to use. The risk stratification indicated that high-risk patients with a risk score >147.9 had a higher chance of pregnancy loss (OR=6.05, 95%CI 4.09-8.97). This nomogram well-predicted the risk of future pregnancy loss in RPL and can be used by clinicians to identify high-risk patients and provide a reference for pregnancy management of RPL.

The Contribution of Road Construction on Regional Economic Development in Indonesia

The development process in Indonesia's provinces is expected to contribute to regional economic growth. This research aims to evaluate road construction's impact on regional economic development and the spatial spillover effect in 34 Indonesian provinces. This paper constructs Spatial Autoregressive Model (SAR), Spatial Durbin Model (SDM), Spatial Autocorrelation Model (SAC), Spatial Error Model (SEM), and Generalised Spatial Random Effect Model (GSPRE) for 34 provinces in Indonesia from 2010 to 2020 to capture the effect of road construction on regional economic development and spatial spillover effect. Compared to other models, the spatial Durbin model has the minimum Akaike information criterion (AIC) value, indicating that it is the most suited model. The result indicates that road infrastructure has little impact on regional economic development. Moreover, road development has a negative value and negligible spatial spillover effect.

ON THE PERFORMANCE OF SARIMA AND SARIMAX MODEL IN FORECASTING MONTHLY AVERAGE RAINFALL IN KOGI STATE, NIGERIA

Forecasting monthly rainfall is very important in Kogi state for better approach to flood management and also plays a pivotal role in agriculture which remains a significant factor in Nigeria’s economy. Advanced time series univariate models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) models are usually employed in modelling and forecasting rainfall in Nigeria due to their non-linear pattern and spatiotemporal variation. Few studies have attempted to investigate the influence of other climatic factors in modelling and prediction of rainfall pattern. This study examines the performance of a univariate seasonal ARIMA and seasonal ARIMA which uses monthly temperature and relative humidity as exogenous factors otherwise known as SARIMAX model in forecasting monthly average rainfall in Lokoja, the capital of Kogi state. The study uses monthly data on rainfall, temperature and relative humidity spanning from 2010 to 2022 obtained from Nigeria Meteorological Agency NiMet, Lokoja station. The series were appropriately differenced to attain stationarity. The plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to select some tentative models whose parameters would be estimated. The most suitable SARIMA model [SARIMA was chosen based on maximum Coefficient of Determination , and the minimum Akaike information criterion (AIC). However, SARIMAX model outperformed SARIMA model based on the criteria earlier highlighted. SARIMAX model was therefore recommended for modelling and forecasting monthly average rainfall in Kogi state.

Experiencing the physical and psychosocial aspects in a multi-bedded room in an oncology ward in the Netherlands before the COVID-19 pandemic

PurposeUntil now, it is not clear whether there are differences in patient perception between multi-bedded rooms with two and four beds. The purpose of this study was to investigate the effect of the physical (i.e. room type) and psychosocial (i.e. kindness of roommates and extraversion) aspects on the patients’ experience (i.e. pleasantness of the room, anxiety, sleep quality) in multi-bedded rooms in an oncology ward.Design/methodology/approachA group of 84 hospitalized oncology patients completed a questionnaire on the day of departure. Room types were categorized into two groups: two-person and four-person rooms.FindingsMultivariate logistic regression analyses with the minimum Akaike Information Criterion (AIC) showed no direct main effects of room type (two vs. four-person room), kindness of roommates and extraversion on pleasantness of the room, anxiety and sleep quality. However, the authors found an interaction effect between room type and extraversion on pleasantness of the room. Patients who score relatively high in extraversion rated the room as more pleasant when they stayed in a four-person rather than a two-person room. For patients relatively low in extraversion, room type was not related to pleasantness of the room.Practical implicationsThe findings allow hospitals to better understand individual differences in patient experiences. Hospitals should inform patients about the benefits of the different room types and potential influences of personality (extraversion) so patients are empowered and can benefit from autonomy and the most appropriate place.Originality/valueThis study emphasizes the importance of including four-person rooms in an oncology ward, while new hospital facility layouts mainly include single-bed rooms.

Pemodelan Indeks Pembangunan Manusia Nusa Tenggara Barat Menggunakan Geographically Weighted Regression

The Human Development Index (HDI) is an indicator for measuring the level of social and economic development of a country or region. The reality is that a local-based model of autonomy is often needed because of the spatial heterogeneity that can occur due to the territory's geographical, social, cultural, or other conditions. This research aims to find spatial effects affecting HDI in West Nusa Tenggara Province. A method that can be used to accommodate is Geographically Weighter Regression (GWR). GWR analysis is the development of multiple linear regression analysis that can address territorial diversity/spatial heterogeneity so as to produce different models and predictions of parameters for each observation region. The modeling was carried out using the Gaussian Kernel Adaptive spatial weigher with an optimal bandwidth value of 27,1227 and a minimum CV value of 5,2927. The GWR model modeling resulted in 10 models for each observation location and showed that life expectancy variables, school expectance, per capita income, and average school-age significantly influenced the IPM in the West Southeast Nusa Province in 2022 with an R2 of 99.92% and a minimum AIC value of -10,0281.

Public health insurance, frailty, and lack of home support predict rehab discharge following elective anterior cervical discectomy and fusion

BACKGROUND CONTEXTAnterior cervical discectomy and fusion (ACDF) is a commonly-performed and generally well-tolerated procedure used to treat cervical disc herniation. Rarely, patients require discharge to inpatient rehab, leading to inconvenience for the patient and increased healthcare expenditure for the medical system. PURPOSEThe objective of this study was to create an accurate and practical predictive model for, as well as delineate associated factors with, rehab discharge following elective ACDF. STUDY DESIGNThis was a retrospective, single-center, cohort study. PATIENT SAMPLEPatients who underwent ACDF between 2012 and 2022 were included. Those with confounding diagnoses or who underwent concurrent, staged, or nonelective procedures were excluded. OUTCOME MEASURESPrimary outcomes for this study included measurements of accuracy for predicting rehab discharge. Secondary outcomes included associations of variables with rehab discharge. METHODSCurrent Procedural Terminology codes identified patients. Charts were reviewed to obtain additional demographic and clinical characteristics on which an initial univariate analysis was performed. Two logistic regression and two machine learning models were trained and evaluated on the data using cross-validation. A multimodel logistic regression was implemented to analyze independent variable associations with rehab discharge. RESULTSA total of 466 patients were included in the study. The logistic regression model with minimum corrected Akaike information criterion score performed best overall, with the highest values for area under the receiver operating characteristic curve (0.83), Youden's J statistic (0.71), balanced accuracy (85.7%), sensitivity (90.3%), and positive predictive value (38.5%). Rehab discharge was associated with a modified frailty index of 2 (p=.007), lack of home support (p=.002), and having Medicare or Medicaid insurance (p=.007) after correction for multiple hypotheses. CONCLUSIONSNonmedical social determinants of health, such as having public insurance or a lack of support at home, may play a role in rehab discharge following elective ACDF. In combination with the modified frailty index and other variables, these factors can be used to predict rehab discharge with high accuracy, improving the patient experience and reducing healthcare costs.

Joint Modeling of Blood Pressure Measurements and Survival Time to Cardiovascular Disease Complication among Hypertension Patients Follow-up at DebreTabor Hospital, Ethiopia.

Hypertension is also referred to as a silent killer and a leading factor for cardiovascular disease complication in the world today. This study aimed to identify the factors that affect longitudinal outcomes and survival time for cardiovascular disease complications among patients with hypertension. A retrospective cohort study was conducted among a randomly selected sample of 178 outpatients with hypertension at the Debre Tabor Specialized Hospital between September 2017 and December 2019. Three different models were used to analyze the data: the bivariate mixed-effects model, Cox proportional hazard model, and bivariate joint model for longitudinal and survival sub-models linked by shared random effects. Bivariate mixed-effects and Cox proportional hazards survival sub-models were jointly preferred based on the minimum Akaike Information Criterion value. The estimated values of the association parameters were 0.0655 (p = 0.0270) and 0.963 (p = 0.0387), indicating that the association between systolic and diastolic blood pressure with time to event was guaranteed. The joint bivariate mixed-effects model analysis showed that patients with hypertension with a family history of hypertension and clinical stage II hypertension have a high chance of developing cardiovascular disease complications and have high average systolic and diastolic blood pressure compared to their counterparts. Patients with hypertension and diabetes have higher systolic and diastolic blood pressure than their counterparts. Generally, systolic and diastolic blood pressure stabilized over the follow-up period of treatment, while sex and residence were statistically insignificant to the survival time of cardiovascular disease complication. Health professionals and concerned bodies should therefore focus on patients with comorbidities, older age, and poor adherence to hypertension control and cardiovascular disease complications using technology, such as text messaging, and mobile application to promote cardiovascular health at early stage. It is important to provide early interventions for these groups of people, especially for those with family history.

Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis

Air pollution is a widespread problem in India. The study focuses on forecasting the air quality index (AQI) using time series modeling techniques for the most polluted area of Dehradun City in Uttarakhand state, India. The train test approach of machine learning and Akaike information criterion (AIC) have been used on the monthly data of five years to select the best auto-regressive model. Using the auto-correlation functions (ACF and PACF) and the seasonality component in the time-series dataset, a seasonal auto-regressive moving average (ARMA) model with its minimum AIC has been chosen to forecast the AQI. This model is also validated by comparing its predicted values with the actual values of AQI. The results showed that the seasonal ARMA model of (1,0,0)(1,0,0)12 could forecast AQI based on a stationary dataset. The research also indicates that the asthma patients of the Himalayan Drugs-ISBT region may experience more health effects, especially in winter, due to poor air quality. The model can be helpful for a scientist and the government to take precautionary measures in advance.