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BACKGROUND: Little is known about patients with sudden cardiac arrest in the emergency department (ED). This study aimed to identify factors affecting the prognosis of patients with cardiac arrest in the ED. METHODS: This retrospective study analyzed patients with sudden cardiac arrest admitted to the ED of a general hospital between January 2016 and July 2020. A total of 153 patients with sudden cardiac arrest were identified, and 149 patients for whom all data could be confirmed were included in the statistical analysis of this study. A good neurological outcome was defined as a Cerebral Performance Category (CPC) scale score of 1 or 2, assessed 6 months after discharge. RESULTS: In the univariate analysis, the characteristics of patients included in the good neurological outcomes group were younger (t = 3.553, p < .001), had shorter low flow time (t = 3.31, p = .019), and had more shockable initial rhythms (χ2 = 28.038, p = < .001). As a result of multivariate binary logistic regression analysis, among 43 patients alive 6 months after discharge, age 60 years or younger (odds ratio = 32.703, p = .005), low flow time 6 min or less (odds ratio = 38.418, p = .006), and initial shockable rhythm (odds ratio = 31.214, p < .001) were identified as predictors that had a significant impact on good neurological outcomes. CONCLUSIONS: Young age, short low-flow-time, and initial shockable rhythm are predictors of good neurological outcomes in patients with acute cardiac arrest in the ED.

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E-government services are essential to societies because they save time, reduce corruption, provide efficient, low-cost, and fast service, increase transparency, and enhance trust in the government. These applications save time, which translates to cost savings by reducing bureaucratic crowds and fatigue and eliminating the need for citizens to travel for offline transactions. This study investigates various factors related to citizens' use of e-government services according to gender differences during and before COVID-19. The microdata set from the Survey on Information and Communication Technology (ICT) Usage in Households conducted by TURKSTAT in 2018 and 2021 was used. Additionally, the binary logistic regression method was employed to analyze these factors. According to the research results, it has been determined that variables such as age, education level, occupation, e-commerce use, internet financial transaction status, number of people in the household, and region are associated with women's use of e-government services during the COVID-19 pandemic. The study found that the significance and impact of these variables on the use of e-government services differ based on the gender of individuals and the periods. The study provides recommendations for IT professionals, staff of the interior ministries, and researchers interested in increasing the use of e-government services. This research may also pioneer efforts to identify priority areas for expanding e-government services.

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Objective: This study aimed to elucidate the risk factors associated with alcohol use disorders (AUDs) among inpatients with schizophrenia at a specialized mental hospital in Baoding city, China. Methods: This cross-sectional survey comprised 301 comorbid patients. Three binary logistic regression models were used to investigate the factors linked to AUDs in patients with schizophrenia. Propensity score matching analysis was conducted to validate inconsistent variables identified by the regression models. Results: Significant differences were observed between the comorbid and non-comorbid groups concerning sex (P < .001), disposition (P = .049), smoking habits (P < .001), place of residence (P = .010), family relationships (P = .002), family history of mental disorders (P = .008), history of alcoholism (P = .003), onset latency (P = .005), impulsivity (P < .001), suicide or self-injury history (P < .001), and obvious aggressive behavior (P < .001) in univariate analyses. The area under the curve values for the three regression models were 0.83 (P < .001), 0.80 (P < .001), and 0.81 (P < .001), respectively. Binary logistic regression and propensity score matching analyses indicated that introverted disposition, smoking, acute onset, impulsivity, and suicide or self-injury history were independent risk factors associated with AUDs in inpatients with schizophrenia with an odds ratio of > 1. Conclusion: Introverted disposition, smoking, acute onset, impulsivity, and suicide or self-injury history were independently associated with the AUDs in inpatients with schizophrenia. Future studies should prioritize longitudinal studies to discern the evolving dynamics of potential confounding risk factors.

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Background and Purpose: The Readiness for Hospital Discharge Scale (RHDS) was created to determine the patient's readiness for discharge to home from the hospital. The purpose of this study is to determine the scale's internal consistency and predictive validity in a skilled nursing facility (SNF) setting. Methods: Participants (N = 30) over the age of 65 were conveniently selected from 10 different SNFs in the Midwest to complete the RHDS prior to discharge. Results: Cronbach's alpha for internal consistency was 0.917. Participants with higher RHDS scores, those who were male, respondents with less education, and participants with Medicare Advantage insurance were more likely to be rehospitalized within 30 days of discharge to home. Conclusions: The results indicate that the RHDS has a good internal consistency in the SNF setting.

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BACKGROUND: Health information consumers can acquire knowledge regarding health problems, combat health problems, make health-related decisions, and change their behaviour by conducting health information searches. This study aims to identify the sociodemographic and economic factors affecting individuals' search for health information on the internet before and during COVID-19. METHODS: In this study, micro data sets of the Household Information Technologies (IT) Usage Survey conducted by the Turkish Statistical Institute in 2018 and 2021 were used. The binary logistic regression analysis was also used in the study. RESULTS: It was determined that age, gender, education level, occupation, social media use, searching for information about goods and services, internet banking use, e-government use, having a desktop computer, having a tablet computer, and region variables were associated with the status of searching for health information on the internet during the COVID-19 period. CONCLUSION: The main reasons for the increase in health information searches during the COVID-19 epidemic can be attributed to several key factors, such as society's need for information and meeting its need for information, access to up-to-date health data and increased trust in official sources. The study's findings serve as a valuable resource for health service providers and information sources attempting to identify the health information-seeking behaviour of the public and to meet their needs in this context.

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OBJECTIVE: Prediction of fetal growth restriction (FGR) and small of gestational age (SGA) infants by using various ultrasound cardiac parameters in a logistic regression model. METHODS: In this retrospective study we obtained standardized ultrasound images of 357 fetuses between the 20th and 39th week of gestation, 99 of these fetuses were between the 3rd and 10th growth percentile, 61 smaller than 3rd percentile and 197- appropriate for gestational age over the 10th percentile (control group). Several cardiac parameters were studied. The cardiothoracic ratio and sphericity of the ventricles was calculated. A binary logistic regression model was developed for prediction of growth restriction using the cardiac and biometric parameters. RESULTS: There were noticeable differences between the control and study group in the sphericity of the right ventricle (p = 0.000), left and right longitudinal ventricle length (pright = 0.000, pleft = 0.000), left ventricle transverse length (p = 0.000), heart diameter (p = 0.002), heart circumference (p = 0.000), heart area (p = 0.000), and thoracic diameter limited by the ribs (p = 0.002). There was no difference of the cardiothoracic ratio between groups. The logistic regression model achieved a prediction rate of 79.4 % with a sensitivity of 74.5 % and specificity of 83.2 %. CONCLUSION: The heart of growth restricted infants is characterized by a more globular right ventricle, shorter ventricle length and smaller thorax diameter. These parameters could improve prediction of FGR and SGA.

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Atrial fibrillation (AF) is more prevalent in individuals with chronic kidney disease (CKD) compared to the general population. While a potential inverse correlation between lipid levels and AF has been proposed, it remains unclear if this relationship applies to CKD patients. This study examined the connection between the ratio of low-density lipoprotein cholesterol to high-density lipoprotein cholesterol (LDL-C/HDL-C) and the likelihood of AF in CKD patients. Data was gathered from 21,091 consecutive CKD patients between 2006 and December 31, 2015. We examined the link between the LDL-C/HDL-C ratio and AF in CKD patients through binary logistic regression, as well as various sensitivity and subgroup analyses. The dataset that backs up these analyses is available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230189 . Of the 21,091 CKD patients, 211 (1.00%) were diagnosed with AF. The cohort, predominantly male (79.93%), had a mean age of 60.89 ± 10.05 years. The mean LDL-C/HDL-C ratio was 1.39 ± 0.35. After adjusting for covariates, a significant inverse association was observed between the LDL-C/HDL-C ratio and the incidence of AF in CKD patients (OR = 0.422, 95% CI 0.273-0.652, P = 0.00010). The robustness of these findings was confirmed through sensitivity analysis. Subgroup analysis revealed a strong correlation between the LDL-C/HDL-C ratio and incident AF in patients without hypertension (HR = 0.26, 95% CI 0.15-0.45). Conversely, this association was absent in hypertensive patients (HR = 1.09, 95% CI 0.54-2.17). Our research shows an independent inverse correlation between the LDL-C/HDL-C ratio and the risk of AF in CKD patients. It is advised to refrain from excessively aggressive reduction of LDL levels in CKD patients, as this could elevate the risk of developing AF.

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Background: Surgical site infection (SSI) is a common complication in HIV-positive fracture patients undergoing surgery, leading to increased morbidity, mortality, and healthcare costs. Accurate prediction of SSI risk can help guide clinical decision-making and improve patient outcomes. However, there is a lack of user-friendly, Web-based calculator for predicting SSI risk in this patient population. Objective: This study aimed to develop and validate a novel web-based risk calculator for predicting SSI in HIV-positive fracture patients undergoing surgery in China. Method: A multicenter retrospective cohort study was conducted using data from HIV-positive fracture patients who underwent surgery in three tertiary hospitals in China between May 2011 and September 2023. We used patients from Beijing Ditan Hospital as the training cohort and patients from Chengdu Public Health and Changsha First Hospital as the external validation cohort. Univariate, multivariate logistic regression analyses and SVM-RFE were performed to identify independent risk factors for SSIs. A web-based calculator was developed using the identified risk factors and validated using an external validation cohort. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (AUC) curves, calibration plots, and decision curve analysis (DCA). Results: A total of 338 HIV-positive patients were included in the study, with 216 patients in the training cohort and 122 patients in the validation cohort. The overall SSI incidence was 10.7%. The web-based risk calculator (https://sydtliubo.shinyapps.io/DynNom_for_SSI/) incorporated six risk factors: HBV/HCV co-infection, HIV RNA load, CD4+ T-cell count, Neu and Lym level. The nomogram demonstrated good discrimination, with an AUC of 0.890 in the training cohort and 0.853 in the validation cohort. The calibration plot showed good agreement between predicted and observed SSI probabilities. The DCA indicated that the nomogram had clinical utility across a wide range of threshold probabilities. Conclusion: Our study developed and validated a novel web-based risk calculator for predicting SSI risk in HIV-positive fracture patients undergoing surgery in China. The nomogram demonstrated good discrimination, calibration, and clinical utility, and can serve as a valuable tool for risk stratification and clinical decision-making in this patient population. Future studies should focus on integrating this nomogram into hospital information systems for real-time risk assessment and management.

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Few studies included objective blood pressure (BP) to construct the predictive model of severe obstructive sleep apnea (OSA). This study used binary logistic regression model (BLRM) and the decision tree method (DTM) to constructed the predictive models for identifying severe OSA, and to compare the prediction capability between the two methods. Totally 499 adult patients with severe OSA and 1421 non-severe OSA controls examined at the Sleep Medicine Center of a tertiary hospital in southern Taiwan between October 2016 and April 2019 were enrolled. OSA was diagnosed through polysomnography. Data on BP, demographic characteristics, anthropometric measurements, comorbidity histories, and sleep questionnaires were collected. BLRM and DTM were separately applied to identify predictors of severe OSA. The performance of risk scores was assessed by area under the receiver operating characteristic curves (AUCs). In BLRM, body mass index (BMI) ≥27 kg/m2, and Snore Outcomes Survey score ≤55 were significant predictors of severe OSA (AUC 0.623). In DTM, mean SpO2 <96%, average systolic BP ≥135 mmHg, and BMI ≥39 kg/m2 were observed to effectively differentiate cases of severe OSA (AUC 0.718). The AUC for the predictive models produced by the DTM was higher in older adults than in younger adults (0.807 vs. 0.723) mainly due to differences in clinical predictive features. In conclusion, DTM, using a different set of predictors, seems more effective in identifying severe OSA than BLRM. Differences in predictors ascertained demonstrated the necessity for separately constructing predictive models for younger and older adults.

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Continual re-evaluation of standards for forensic anthropological analyses are necessary, particularly as new methods are explored or as populations change. Indian South Africans are not a new addition to the South African population; however, a paucity of skeletal material is available for analysis from medical school collections, which has resulted in a lack of information on the sexual dimorphism in the crania. For comparable data, computed tomography scans of modern Black, Coloured and White South Africans were included in addition to Indian South Africans. Four cranial morphoscopic traits, were assessed on 408 modern South Africans (equal sex and population distribution). Frequencies, Chi-squared tests, binary logistic regression and random forest modelling were used to assess the data. Males were more robust than females for all populations, while White South African males were the most robust, and Black South African females were the most gracile. Population differences were noted among most groups for at least two variables, necessitating the creation of populations-specific binary logistic regression equations. Only White and Coloured South Africans were not significantly different. Indian South Africans obtained the highest correct classifications for binary logistic regression (94.1%) and random forest modelling (95.7%) and Coloured South Africans had the lowest correct classifications (88.8% and 88.0%, respectively). This study provides a description of the patterns of sexual dimorphism in four cranial morphoscopic traits in the current South African population, as well as binary logistic regression functions for sex estimation of Black, Coloured, Indian and White South Africans.

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Previous research emphasizes the significance of key performance metrics in determining match outcomes. The purpose of this study is to enhance the understanding of success in professional soccer by analyzing the relationship between match outcomes (win, lose, draw) and various Performance Indicators extracted from the Greek soccer league, as well as to develop a regression model of success in soccer. The sample consisted of all 91 matches from the first round of the 2020-2021 season of the Greek Football League. Utilizing Kruskal-Wallis tests, significant differences were found in goals scored, shots, and shots on target, ball possession, passing metrics, touches in the penalty area, and average shot distance (p < 0.05), with winning teams having demonstrated superior performance metrics. Moreover, winning teams engaged more in positional attacks and counterattacks with shots (p < 0.05). The binary logistic regression model applied to predict match outcomes identified shots on target, counterattacks, passes metrics, offensive duels and set pieces (penalties, free kicks) as key factors influencing the likelihood of winning (p < 0.05). These findings collectively highlight the importance of effective offensive play, including goal scoring, shooting accuracy, and ball possession, in determining the outcomes of soccer matches, with the regression model offering a nuanced understanding of these relationships.

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The COVID-19 pandemic has significantly impacted the tourism sector, particularly tour guides (TGs), affecting their professional identity (TGPI) and intentions to return to work. As China strives to revive its tourism industry, it is crucial to understand the current state of TGPI, its evolution, influencing factors, and its impact on TGs' return intentions. This study employed a quantitative approach, using comparative analysis and binary logistic regression, to investigate these issues among frontline TGs in China, pre- and post-pandemic. Cross-sectional surveys were conducted with 422 participants in 2019 and 398 in 2022, yielding 370 and 342 valid responses, respectively. The questionnaire utilized a five-point Likert scale. Findings reveal that (1) The overall TGPI level in 2022 post-pandemic is medium (3.93), showing a significant decrease from the pre-pandemic level in 2019 (4.15). (2) Influencing factors of TGPI are predominantly material, reflected in social insurance and income changes pre- and post-pandemic. (3) This study presents a novel definition and scale of TGPI, encompassing tour guides' professional value identity (TGPVI), emotion identity (TGPEI), relationship identity (TGPRI), and behavior tendency (TGPBT). (4) The two dimensions of the TGPI, TGPVI and TGPRI, income and education level, significantly influence TGs' return intentions. The study provides valuable academic and practical insights into TGPI and offers significant implications for enhancing TGs' return intentions and policymaking for post-pandemic tourism industry development.

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INTRODUCTION: Reproductive health service (RHS) helps for people to have a delighted and safe sex through their life journey. It enables especially for women to go safely through pregnancy and childbirth and provide couples with the best chance of having a healthy infant. Therefore, this study aimed to identify the significant determinants of RHS utilization among undergraduate regular class students in Assosa University by using advanced methodology. METHODS: We used cross-sectional study design to collect RHS data from 362 students in Assosa University from 5 to 16, may 2021. These students were selected using stratified random sampling technique. We also used cross-tabulation to summarize the extents of RHS utilization across all predictors in terms of percentage and three varieties of multilevel binary logistic regression model to model the determinants of RHS. RESULTS: 42.27% of undergraduate regular class students in Assosa University utilize at least one type of RHS during their time at Assosa University whereas, 57.73% of undergraduate regular class students in this University are not utilized it. Among three varieties of multilevel binary logistic regression models, the random slopes two-level model was selected as a best fitted model for the datasets. At 5% level of significance, awareness about RHS, gender, preference of service fees and student's monthly average income were significant predictor variables in this model. In addition, the covariates; age, gender and preference of service fees have a significant random effects on utilization of RHS across all colleges/school. CONCLUSION: Students who; preferred service fee as usual rate, have awareness about RHS, are females and have high monthly average income were more likely to utilize RHS. RHS utilization among undergraduate regular students in Assosa University is likely to increase more effectively with interventions that address these factors.

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This study investigates the possibility of utilizing drip as a non-destructive method for assessing the freshness and spoilage of chicken meat. The quality parameters [pH, volatile base nitrogen (VBN), and total aerobic bacterial counts (TAB)] of chicken meat were evaluated over a 13-day storage period in vacuum packaging at 4 °C. Simultaneously, the metabolites in the chicken meat and its drip were measured by nuclear magnetic resonance. Correlation (Pearson's and Spearman's rank) and pathway analyses were conducted to select the metabolites for model training. Binary logistic regression (model 1 and model 2) and multiple linear regression models (model 3-1 and model 3-2) were trained using selected metabolites, and their performance was evaluated using receiver operating characteristic (ROC) curves. As a result, the chicken meat was spoiled after 7 days of storage, exceeding 20 mg/100 g VBN and 5.7 log CFU/g TAB. The correlation analysis identified one organic acid, eight free amino acids, and five nucleic acids as highly correlated with chicken meat and its drip during storage. Pathway analysis revealed tyrosine and purine metabolism as metabolic pathways highly correlated with spoilage. Based on these findings, specific metabolites were selected for model training: ATP, glutamine, hypoxanthine, IMP, tyrosine, and tyramine. To predict the freshness and spoilage of chicken meat, model 1, trained using tyramine, ATP, tyrosine, and IMP from chicken meat, achieved a 99.9 % accuracy and had an ROC value of 0.884 when validated using drip metabolites. This model 1 was improved by training with tyramine and IMP from both chicken meat and its drip (model 2), which increased the ROC value for drip metabolites from 0.884 to 0.997. Finally, selected two metabolites (tyramine and IMP) can predict TAB and VBN quantitatively through models 3-1 and 3-2, respectively. Therefore, the model developed using metabolic changes in drip demonstrated the capability to non-destructively predict the freshness and spoilage of chicken meat at 4 °C. To make generic predictions, it is necessary to expand the model's applicability to various conditions, such as different temperatures, and validate its performance across multiple chicken batches.

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The Chure region, among the world's youngest mountains, stands out as highly susceptible to natural calamities, particularly forest fires. The region has consistently experienced forest fire incidents, resulting in the degradation of valuable natural and anthropogenic resources. Despite its vulnerability, there have been limited studies to understand the relationship of various causative factors for the recurring fire problem. Hence, to comprehend the influencing factors for the recurring forest fire problem and its extent, we utilized generalized linear modeling under binary logistic regression to combine the dependent variable of satellite detected fire points and various independent variables. We conducted a variance inflation factor (VIF) test and correlation matrix to identify the 14 suitable variables for the study. The analysis revealed that forest fires occurred mostly during the three pre-monsoon periods and had a significant positive relation with the area under forest, rangeland, bare-grounds, and Normalized Difference Vegetation Index (NDVI) (P < 0.05). Consequently, our model showed that the probability of fire incidents decreases with elevation, precipitation, and population density (P < 0.05). Among the significant variables, the forest areas emerges as the most influencing factor, followed by precipitation, elevation, area of rangeland, population density, NDVI, and the area of bare ground. The validation of the model was done through the area under the curve (AUC = 0.92) and accuracy (ACC = 0.89) assessments, which showed the model performed excellently in terms of predictive capabilities. The modeling result and the forest fire susceptible map provide valuable insights into the forest fire vulnerability in the region, offering baseline information about forest fires that will be helpful for line agencies to prepare management strategies to further prevent the deterioration of the region.

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Divorce is a common occurrence in the marital lives of spouses. Consequently, numerous divorced spouses and their children face various social, economic, physiological, and health problems after breaking their marriage. This study aimed to identify the predictors of divorce and the duration of marriage. We conducted a community-based cross-sectional study among 423 randomly selected residents of Dejen Township in April 2020, of which only 369 respondents met the study inclusion criteria. We used structured questionnaires to collect data. The predictors of divorce and duration of marriage were analyzed using binary logistic regression and the Gompertz regression model, respectively. A p value less than 0.05 was used to express statistical significance. The prevalence of divorce was 21.14% [95% CI (19.01-23.27%)]. Half of these women broke up their marriage after 11 years. A high age difference (7 or more years) between spouses, an early marriage, infertility among women, the presence of third parties, women without formal education, women in the workforce, sexually dissatisfied women, women who did not live together with their husbands at the same address, partner violence, marital control behaviour of husbands, drug-abused husbands, spouses without children, and women who knew multiple sexual partners were the significant predictors of divorce. Partner violence, sexually dissatisfied women, women who made their own marriage decisions, marital control behaviour of husbands, women who did not live together with their husbands at the same address, drug-abused husbands and spouses without children were significant predictors of shorter marriage durations. In this study, the prevalence of divorce was high. Therefore, a community-based, integrated strategy is needed to minimize the divorce rate.

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Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERß dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERß agonists among OH-PCB congeners.

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Background/aim: Mental disorders pose a substantial public health challenge within the overall disease burden. This study aims to determine the factors associated with seeking psychological help among individuals experiencing depression according to gender differences in Türkiye. Methods: The study utilized microdata from Türkiye Health Survey conducted by the Turkish Statistical Institute in 2016, 2019, and 2022. Binary logistic regression analysis was employed to determine the factors associated with seeking psychological help. Results: The study's findings reveal that variables such as survey year, age, education level, employment status, general health status, disease status, depression status, day service status in the hospital, daily activity status, tobacco use status, and alcohol use status are associated with the status of receiving psychological help. Conclusion: Gender-specific analysis indicated variations in the significance and impact of these variables among individuals seeking psychological help. In the development of preventive strategies for mental health protection, special attention should be given to factors associated with the psychological help-seeking behavior of both women and men. Prioritizing and addressing these factors will contribute to more effective mental health interventions.

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This article examines the efficacy of volunteering potential regarding actual volunteering at a later point in time. Volunteering potential consists of two components: past volunteering and the intention to do so in the future. Using two-wave panel data from the German Transitions and Old Age Potential (TOP) study with 1,196 respondents born between 1942 and 1958, binary logistic regression estimates reveal significant adjusted average marginal effects of both potential components on actual volunteering at a later stage (intention: +8.4 percentage points, past volunteering: +6.3 percentage points). Considering both components as an interaction term, analyses provide mixed results. By taking greater account of the potential volunteers, scholars and policy-makers will have better insights into how to assess recruitment potential among older adults.

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Understanding the drivers of urban growth and spatiotemporal land use change is important for rational land use and sustainable urban development. Based on the land use data, GIS data of explanatory variables, experts' knowledge and field observation, the study used a binary logistic regression model (BLRM) to analyze factors that drive rapid urban growth in Bahir Dar city, Ethiopia, using the LOGISTICREG module in IDRISI Selva software. Nine factors were used to reflect the influence of proximity and physical factors on urban growth from 1984 to 2019. This model helped in quantifying and identifying the factors of urban growth, which includes topography (slope, elevation and aspect) and accessibility (Dis. to the main road, Dis. to international airport, Dis. to CBD, Dis. to existing built-up area, Dis. to forest land and Dis. to water body). Furthermore, urban growth probability maps were created based on LRM results, revealing that the biggest urban growth would occur around existing built-up areas along the main roads and near Bahir Dar international airport. The Relative Operating Characteristic (ROC) values of 0.85, 0.90 and 0.93 and PCP values of 96.72 %, 98.46 % and 98.51 % indicate the urban growth probability maps are valid and BLRM had an ideal ability to predict urban growth. So, the study highlighted the relation between urban growth and its drivers in Bahir Dar, giving a decision making framework for better land use management and resource allocation.