Interpersonal protective factors for LGBTQ+ youth at multiple intersecting social identities and positions.

Interpersonal supports are protective against multiple negative health outcomes for youth such as emotional distress and substance use. However, finding interpersonal support may be difficult for youth exposed to intersecting racism, heterosexism, and cisgenderism, who may feel they are "outsiders within" their multiple communities. This study explores disparities in interpersonal supports for youth at different sociodemographic intersections. The 2019 Minnesota Student Survey includes data from 80,456 high school students, including measures of four interpersonal supports: feeling cared about by parents, other adult relatives, friends, and community adults. Exhaustive Chi-square Automatic Interaction Detection analysis was used to examine all interactions among four social identities/positions (racialized/ethnic identity, sexual identity, gender identity, sex assigned at birth) to identify groups who report different rates of caring from each source (Bonferroni adjusted p<.05). In the overall sample, 69.24% perceived the highest level of caring ("very much") from parents, 50.09% from other adult relatives, 39.94% from friends, and 15.03% from community adults. Models identified considerable differences in each source of support. For example, more than 72% of straight, cisgender youth reported their parents cared about them very much, but youth who identified as LGBQ and TGD or gender-questioning were much less likely to report high parent caring (less than 36%) across multiple racialized/ethnic identities and regardless of sex assigned at birth. Findings highlight the importance of better understanding the ways interpersonal support might differ across groups, and underscore a need for intersectionality-tailored interventions to develop protective interpersonal supports for LGBTQ+ youth, rather than one-size-fits-all approaches.

State-Federal Vocational Rehabilitation Services, Demographic Characteristics and Employment Outcomes for Native Americans with Mental Illnesses.

There were 9.7 million Native Americans (American Indian, Alaska Native-AI/AN- these acronyms will be used interchangeably with Native Americans throughout the paper) in 2019 comprising 2.9% of the U.S. population. Native American populations have disproportionately higher rates of mental illnesses compared to other racial groups in the U.S. Mental health is a significant public health concern for this population, impacting different areas of their lives including employment. Additionally, Native Americans continue to experience significant disparities in access to Vocational Rehabilitation (VR) services and have poor employment outcomes. However, little is known about the relationships among demographic factors, vocational rehabilitation services, and employment outcomes of Native Americans with mental illness. Consequently, the current study examined how demographic factors and VR services are related to successful employment outcomes for Native American VR clients with mental illnesses using data from the Rehabilitation Services Administration (RSA) program year (2019) Case Service Report (9-11). Both descriptive analysis and data mining approaches were used to answer the research questions. Chi-square Automatic Interaction Detector (CHAID) analysis was used to determine which of the VR services could best predict the successful employment outcome of Native Americans with mental illness. The findings of the data mining approach revealed that among all the vocational rehabilitation services, job placement assistance was the strongest predictor of successful employment among Native American clients with mental illnesses. The second most important service predicting successful employment for those who received job placement assistance was shown to be maintenance. Implications for rehabilitation counselors and future research are discussed.

Morphological characterization of the Polatli sheep in terms of live weight using data mining algorithms.

The aim of this research is both to estimate the live weight (LW) of Polatli sheep (Ile de France × Akkaraman (G1)) by considering some body measurements (withers height (WH), rump height (RH), body length (BL), chest depth (CD), chest width (CD), chest girth (CG), cannon bone circumference (CBC)), age, and sex variables as independent variables using C&RT (Classification and Regression Tree), CHAID (Chi-square Automatic Interaction Detector), and MARS (Multivariate Adaptive Regression Splines) algorithms and to determine the significant independent variables in the estimation of live weight. For this purpose, a total of 210 sheep were used, including 180 females and 30 males of different ages, for the estimation of LW. The calculated Pearson correlation coefficients between LW and WH, RH, BL, CD, CW, CG, and CBC characteristics are 0.897, 0.896, 0.853, 0.948, 0.550, 0.914, and 0.798, respectively (p < 0.05). In the application of data mining algorithms as prediction models, a cross-validation of 10 was used, while for tree-based algorithms, the parent node was set to 10 and the child node to 5. While CHAID and C&RT algorithms each used 8 independent variables to explain the variation observed in LW, the MARS algorithm used 9 independent variables. In Polatli sheep, the sheep with the highest live weight was found in the node with age > 3 and CD > 36 cm cutting point in the CHAID algorithm (93.571 kg). In the C&RT algorithm, it was predicted to be (91.316 kg) when age > 0, CD > 36.5 cm, and CBC > 9.5 cm. When evaluated considering commonly used criteria, the prediction performances of CHAID, C&RT, and MARS algorithms were calculated as follows: RMSE (root mean square error) values are "5.788, 5.103, 4.005"; SDR (standard deviation ratio) values are "0.254, 0.224, 0.176"; MAPE (mean absolute percentage error) values are "7.555, 6.675, 5.682"; Adj-Rsq (adjusted R-squared) values are "0.935, 0.950, 0.969"; and AIC (Akaike information criterion) values are "741.436, 688.489, 582.792," respectively. In terms of prediction performance, among the tree algorithms (CHAID and C&RT), C&RT was found to be the best, while considering all performance measures, it was observed that the MARS algorithm exhibited the best performance. Consequently, it has been determined that C&RT and MARS algorithms can be safely employed in morphological characterization studies for the identification of indirect criteria and the formation of elite herds in terms of LW. This decision allows for the reliable use of these algorithms to facilitate the selection of indirect variables and the establishment of elite populations in breeding programs focusing on live weight.

Predictive Rule for Mortality of Inpatients With Escherichia coli Bacteremia: Chi-Square Automatic Interaction Detector Decision Tree Analysis Model.

AIM: A predictive rule for risk factors for mortality due to Escherichia coli (E. coli)bacteremia has not been defined, especially using the chi-square automatic interaction detector (CHAID) decision tree analysis. Here we aimed to create the predictive rule for risk factors for in-hospital mortality due to E. coli bacteremia. METHODS: The outcome of this retrospective cross-sectional survey was death in the hospital due to E. coli bacteremia. Factors potentially predictive of death in the hospital due to E. coli bacteremia were analyzed using the CHAID decision tree analysis. RESULTS: A total of 420 patients (male:female=196:224; mean±standard deviation [SD] age, 75.81±13.13 years) were included in this study. 56 patients (13.3%) died in the hospital. The CHAID decision tree analysis revealed that patients with total protein level ≤5.10 g/dL (incidence, 46.2%), total protein level ≤5.90 g/dL with disturbance of consciousness (incidence, 39.4%), and total protein level >5.90 g/dL with hemoglobin level ≤11.10 g/dL and lactate dehydrogenase level ≥312.0 IU/L (incidence, 42.3%) were included in the high-risk group. CONCLUSIONS: Appropriate preventative therapy should be facilitated in patients with E. coliat a high risk of mortality.

Investigation of the Relationships between Coat Colour, Sex, and Morphological Characteristics in Donkeys Using Data Mining Algorithms.

This study was carried out in order to determine the morphological characteristics, body coat colour distribution, and body dimensions of donkeys raised in Turkey, as well as to determine the relationships between these factors. For this reason, the predictive performance of various machine learning algorithms (i.e., CHAID, Random Forest, ALM, MARS, and Bagging MARS) were compared, utilising the biometric data of donkeys. In particular, mean measurements were taken from a total of 371 donkeys (252 male and 119 female) with descriptive statistical values as follows: height at withers, 100.7 cm; rump height, 103.1 cm; body length, 103.8 cm; chest circumference, 112.8 cm; chest depth, 45.7 cm; chest width, 29.1 cm; front shin circumference, 13.5 cm; head length, 55 cm; and ear length, 22 cm. The body colour distribution of the donkeys considered in this study was calculated as 39.35% grey, 19.95% white, 21.83% black, and 18.87% brown. Model fit statistics, including the coefficient of determination (R2), mean square error, root-mean-square error (RMSE), mean absolute percentage error (MAPE), and standard deviation ratio (SD ratio), were calculated to measure the predictive ability of the fitted models. The MARS algorithm was found to be the best model for defining the body length of donkeys, with the highest R2 value (0.916) and the lowest RMSE, MAPE, and SD ratio values (2.173, 1.615, and 0.291, respectively). The experimental results indicate that the most suitable model is the MARS algorithm, which provides a good alternative to other data mining algorithms for predicting the body length of donkeys.

Predictors of self-reported sexually transmitted infections among Turkish university students.

BACKGROUND AND AIM: Sexually transmitted infections (STIs) are among the most crucial health problems that sexually active university students may encounter. This study aims to identify the predictors of self-reported STIs among university students. METHODS: Study participants were 2,241 individuals who reported having had sexual intercourse among a cohort of 9,693 students surveyed from 21 Turkish universities. The age of participants ranged from 17 years to 28 years. RESULTS: The Chi-Square Automatic Interaction Detection (CHAID) analysis indicated that gender was the main predictor of self-reported STI. Also, number of partners and substance use were found as predictor variables for males. Finally, the classification accuracy of the CHAID model was found to be 95.3% within the sample. CONCLUSIONS: The present findings shed light on risk factors for STI acquisition, suggesting possible directions for tailoring future preventive interventions.

Preschool teachers display a flexible pattern of pedagogical actions in promoting healthy habits in children.

The school represents the optimal setting for promoting the physical, emotional, and social health of children, especially during the first years of life. Understanding the pedagogical actions of teachers to address health education is an important first step in promoting healthy behaviors in children. We inhere analyzed the pedagogical action patterns in the preschool teaching of healthy habits from a holistic health perspective. We used photography as a strategy for data collection and applied a Chi-square automatic interaction detection (CHAID) classification tree, a data mining procedure, to generate a pattern model. We found that the school space and the learning playfulness strategies for the development of executive functions, classified according to the exercise, symbolic, assembly, rules (ESAR) model, were the main factors that influence the pedagogical actions fostering healthy habits. By contrast, the school and the pedagogical resources of the classroom are factors with a much smaller impact on working with healthy habits. This pedagogical action pattern is flexible, since teachers conduct a multiplicity of pedagogical actions through different strategies, in different school spaces, at any time. In conclusion, our results unmask the interdependent relationships between the different factors that determine the teacher's actions at the preschool. It also contributes to the understanding of the teacher's practices in fostering healthy habits in a healthy learning environment.

Predictive power of the DSM-5 criteria for internet use disorder: A CHAID decision-tree analysis.

Introduction: Although the majority of internet users enjoy the internet as a recreational activity, some individuals report problematic internet use behaviors causing negative psychosocial consequences. Therefore, it is important to have precise and valid diagnostic criteria to ensure suitable treatment for those affected and avoid over-pathologization. Methods: The aim of the present study was to determine which of the nine DSM-5 criteria of internet gaming disorder (IGD) are crucial in distinguish pathological from non-pathological internet use based on the questionnaire-based response behavior of the participants by applying the Chi-squared automatic interaction detection (CHAID) decision tree analysis. Under consideration of the nine DSM-5 criteria for IGD and according to the short-form scale to assess Internet Gaming Disorder (IGDS-SF9) the DSM-5 criteria were formulated as questions and applied to the broader concept of Internet Use Disorder (IUD). The nine questions were answered on a 5-point Likert scale from "never" to "very often." In accordance with the IGDS-SF9 participants were assigned to IUD-5plus if at least 5 of the 9 criteria were answered with "very often." The study was conducted in Germany (N = 37,008; mean age: 32 years, SD = 13.18, 73.8% male). Results: Although "loss of control," "continued overuse" and "mood regulation" were the most endorsed criteria, the analysis indicated that the criterion "jeopardizing" was found as the best predictor for IUD-5plus, followed by "loss of interest" and "continued overuse." Overall 64.9% of all participants who were in the IUD-5plus, could been identified by the fulfillment of the three criteria mentioned above. Discussion: The results found support for adjustment of the DSM-5 criteria of IGD in accordance to ICD-11. If the predictive power of the three criteria can be replicated in future representative studies, such a decision tree can be used as guidance for diagnostics to capture the particularly relevant criteria.

Effect of ferritin, INR, and D-dimer immunological parameters levels as predictors of COVID-19 mortality: A strong prediction with the decision trees.

Background and objective: A hyperinflammatory environment is thought to be the distinctive characteristic of COVID-19 infection and an important mediator of morbidity. This study aimed to determine the effect of other immunological parameter levels, especially ferritin, as a predictor of COVID-19 mortality via decision-trees analysis. Material and method: This is a retrospective study evaluating a total of 2568 patients who died (n = 232) and recovered (n = 2336) from COVID-19 in August and December 2021. Immunological laboratory data were compared between two groups that died and recovered from patients with COVID-19. In addition, decision trees from machine learning models were used to evaluate the performance of immunological parameters in the mortality of the COVID-19 disease. Results: Non-surviving from COVID-19 had 1.75 times higher ferritin, 10.7 times higher CRP, 2.4 times higher D-dimer, 1.14 times higher international-normalized-ratio (INR), 1.1 times higher Fibrinogen, 22.9 times higher procalcitonin, 3.35 times higher troponin, 2.77 mm/h times higher erythrocyte-sedimentation-rate (ESR), 1.13sec times longer prothrombin time (PT) when compared surviving patients. In addition, our interpretable decision tree, which was constructed with only the cut-off values of ferritin, INR, and D-dimer, correctly predicted 99.7% of surviving patients and 92.7% of non-surviving patients. Conclusions: This study perfectly predicted the mortality of COVID-19 with our interpretable decision tree constructed with INR and D-dimer, especially ferritin. For this reason, we think that it may be important to include ferritin, INR, and D-dimer parameters and their cut-off values in the scoring systems to be planned for COVID-19 mortality.

Predicción de diabetes mellitus basada en el índice triglicéridos y glucosa / Diabetes mellitus prediction based on the triglyceride and glucose index

Introducción: En México la diabetes mellitus tipo 2 (DM2) presenta niveles epidemiológicos, con una tasa de prevalencia del 9,12% y con los índices de sobrepeso y obesidad más altos del mundo. Para superar esta situación se deben crear estrategias enfocadas en la identificación de sujetos en riesgo. El índice triglicéridos y glucosa (TyG) fue creado para la detección de la resistencia a la insulina, y recientemente se ha empleado en la predicción de diabetes mellitus. El objetivo del presente estudio fue determinar el poder predictivo del índice TyG en una cohorte de la Ciudad de México.MétodosSe seleccionaron 3.195 pacientes de una cohorte de pacientes del área de crónico degenerativos de los Centros de Salud de los Servicios de Salud Pública de la Ciudad de México. Se evaluó la capacidad del índice TyG en la predicción de diabetes calculado como: ln (triglicéridos en ayunas [mg/dl]×glucosa en ayunas [mg/dl]/2) después de un seguimiento de al menos 4,5 años. Se determinó una prueba Chi-squared automated interaction detector analysis, que fue corroborada por una prueba ROC.ResultadosEl valor del índice de TyG fue significativamente mayor para los pacientes que desarrollar DM2. Los valores de área bajo la curva=0,934, intervalo de confianza (IC) 95%=0,924-0,924. Obteniendo un punto de corte de 9,45 en mujeres; en hombres: DM AUC=0.824, IC 95%=0,824-0,873 punto de corte 9.12.ConclusionesEl índice TyG es un buen marcador en la predicción de DM2 respaldado por la aplicación del algoritmo CHAID como herramienta útil para la predicción de DM2. (AU)

Introduction: In Mexico, type 2 Diabetes mellitus (DM2) presents epidemiological levels with a prevalence rate of 9.12% and with the highest overweight and obesity rates worldwide. To overcome this situation, strategies must be created focused on the identification of subjects at risk. The Triglyceride and Glucose (TyG) index, was created for the detection of insulin resistance, has recently been used in the prediction of DM. The objective of the present study was to determine the predictive power of the TyG index in a cohort from Mexico City.Methods3195 patients were selected from a cohort of patients from the chronic degenerative area of the Health Centers of the Public Health Services of Mexico City. The ability of the TyG index in predicting diabetes was evaluated as: ln [Fasting triglycerides (mg/dl) x fasting glucose (mg/dl)/2]. after a follow-up of at least 4.5 years. A CHAID test was determined that was corroborated by a ROC test.Resultsthe value of the TyG index was significantly higher for patients who develop DM2. Values of AUC=0.934, 95% CI: 0.924-0.924. Obtaining a cut-off point of 9.45 in women; in men: DM2 AUC=0.824, 95% CI: 0.824-0.873, and cut-off point 9.12.ConclusionsThe TyG index is a good marker in the prediction of DM2. The CHAID determination is a useful tool in the prediction of DM2. (AU)

Social network analysis: Understanding nurses' advice-seeking interactions.

AIM: This study aimed to determine advice-seeking interactions of nurses in a private hospital by using social network analysis. DESIGN: This study was designed as a cross-sectional descriptive study. METHODS: The study was conducted in a private hospital with 70 nurses. The data were collected with a social network analysis questionnaire. The social network analysis (SNA) focused on certain values such as network density, component, degree centrality, and betweenness centrality. The SNA was carried out using UCINET, and statistical analyses were performed with SPSS version 23.0. RESULTS: The network density was reported to be 0.062, and it was composed of three components. It was further noted that nurse Y1 was found to have the highest scores of degree and betweenness centrality. Chi-Square Automatic Interaction Detector (CHAID) analysis indicated that the most common variables that affected degree centrality score were education, department, and position. CONCLUSION: It was concluded that social network analysis was a useful instrument to delineate strengths and weaknesses of seeking advice relationships among nurses. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Top- and middle-level nursing managers occupy a significant position in advice-seeking networks. Nursing managers with higher education degrees will absolutely improve advice-seeking networks.

[Diabetes mellitus prediction based on the triglyceride and glucose index]. / Predicción de diabetes mellitus basada en el índice triglicéridos y glucosa.

INTRODUCTION: In Mexico, type 2 Diabetes mellitus (DM2) presents epidemiological levels with a prevalence rate of 9.12% and with the highest overweight and obesity rates worldwide. To overcome this situation, strategies must be created focused on the identification of subjects at risk. The Triglyceride and Glucose (TyG) index, was created for the detection of insulin resistance, has recently been used in the prediction of DM. The objective of the present study was to determine the predictive power of the TyG index in a cohort from Mexico City. METHODS: 3195 patients were selected from a cohort of patients from the chronic degenerative area of the Health Centers of the Public Health Services of Mexico City. The ability of the TyG index in predicting diabetes was evaluated as: ln [Fasting triglycerides (mg/dl) x fasting glucose (mg/dl)/2]. after a follow-up of at least 4.5 years. A CHAID test was determined that was corroborated by a ROC test. RESULTS: the value of the TyG index was significantly higher for patients who develop DM2. Values of AUC=0.934, 95% CI: 0.924-0.924. Obtaining a cut-off point of 9.45 in women; in men: DM2 AUC=0.824, 95% CI: 0.824-0.873, and cut-off point 9.12. CONCLUSIONS: The TyG index is a good marker in the prediction of DM2. The CHAID determination is a useful tool in the prediction of DM2.

A hierarchy of correlates for objectively measured physical activity, sedentary time, and physical fitness in older adults: A CHAID analysis.

The aging process reflects, in many cases, not only a decline in physical activity (PA) and physical fitness (PF), but also an increase in overall levels of sedentary time (ST). In order to hierarchically identify the most powerful correlates related to low and high levels of objectively assessed PA, ST, and PF during the late adulthood, a total of 2666 older adults were cross-sectionally evaluated. Multidimensional correlates were obtained through interview. Using chi-squared automatic detection analysis to identify the cluster of correlates with most impact on PA (<21.4 min/day), ST (≥8 h/day), and PF (<33.3th percentile), was found that the most likely subgroup to be physically inactive consisted of widowers not owning a computer and sport facilities in the neighbourhood (94.7%), while not being widowed, reporting to have a family that exercises and a computer at home (54.3%) represented the subgroup less likely to be inactive. Widowers without sidewalks in the neighbourhood were the most sedentary group (91.0%), while being a married woman and reporting to have space to exercise at home (40%) formed the most favourable group of correlates regarding ST. Men reporting a financial income <500€ and physical problems frequently formed the group with the lowest PF level (70.3%). In contrast, the less likely subgroup to have low levels of PF level consisted of having a financial income ≥1000€ and a computer at home (3.4%). Future interventions should target widowers with limited accessibility to computer and urban/sport-related infrastructures, as well as impaired older adults with low financial income.HighlightsChi-squared automatic interaction detection was used to identify and hierarchise correlates of objectively measured physical activity, sedentary time, and fitness.Widowers not having a computer at home and sport facilities in the neighbourhood were the most likely to be physically inactive, while not being widowed, having a family that exercises and a computer at home represented the subgroup less likely to be physically inactive.The most likely to be classified as sedentary were widowers without sidewalks in the neighbourhood, while the most favourable group of correlates regarding ST was formed by married women and reporting to have space to exercise at home.Individuals with a low financial income and physical problems comprised the population subgroup with the lowest PF levels, while having a medium-high financial income and a computer at home represented the less likely subgroup to have low levels of PF.

Steroid Resistance Associated with High MIF and P-gp Serum Levels in SLE Patients.

Approximately 30% of patients with systemic lupus erythematosus (SLE) present steroid resistance (SR). Macrophage migration inhibition factor (MIF) and P-glycoprotein (P-gp) could be related to SR. This work aims to evaluate the relationship between MIF and P-pg serum levels in SR in SLE. Methods: Case−control study including 188 SLE patients who were divided into two groups (90 in the steroid-resistant group and 98 in the steroid-sensitive (SS) group) and 35 healthy controls. MIF and P-gp serum levels were determined by ELISA. Multivariable logistic regression and chi-squared automatic interaction detection (CHAID) were used to explore risk factors for SR. Results: The steroid-resistant group presented higher MIF and P-gp serum levels in comparison with the SS (p < 0.001) and reference (p < 0.001) groups. MIF correlated positively with P-gp (rho = 0.41, p < 0.001). MIF (≥15.75 ng/mL) and P-gp (≥15.22 ng/mL) were a risk factor for SR (OR = 2.29, OR = 5.27). CHAID identified high P-gp as the main risk factor for SR and high MIF as the second risk factor in those patients with low P-gp. Conclusions: An association between MIF and P-gp serum levels was observed in SR. CHAID identified P-gp ≥ 15.22 ng/mL as the main risk factor for SR. More studies are needed to validate these results.

Factors relating to childbirth self efficacy among pregnant women: a CHAID analysis.

Childbirth self-efficacy is a useful measure for determining a woman's confidence in managing childbirth and for determining any preconceptions that require reinforcement. Childbirth self-efficacy is also particularly helpful in advising not only how to cope with birth, but also maternal well-being and fostering the improvement of a wide variety of perinatal outcomes. The present study aims to determine the factors affecting childbirth self-efficacy in pregnant women. The sample size consisted of 380 pregnant women between the ages of 18 and 45. Data were collected via face to face interviews using the Childbirth Self Efficacy Scale Short Form (CBSEI-32) in the Akdeniz University Hospital between November 2019 and February 2020 and used Chi-squared Automatic Interaction Detector analyses, resulting in a mean CBSEI-32 score of 244.279 ± 45.121. As a result of the analysis, it was seen that income status affects self-efficacy, and personal experiences such as foetal loss affect a woman's childbirth self-efficacy. In addition, it was also found that the level of prenatal education affected childbirth self-efficacy. Health professionals should assess pregnant women during the antenatal period in terms of their childbirth self efficacy and prepare personalised training programs and plan initiatives to increase perceptions of self-efficacy.IMPACT STATEMENTWhat is already known on this subject? Childbirth self-efficacy is one of the important psychological parameters to determine a woman's belief in her confidence in managing childbirth and to measure women's perceptions of her need for reinforcement.What do the results of this study add? Sociodemographic and obstetric characteristics of women affect their childbirth self-efficacy perception positively and negatively. Women's birth self-efficacy can be improved positively with prenatal education. In addition, it is one of the interesting findings of the study that the self-efficacy level of women who had a previous low experience was high.What are the implications of these findings for clinical practice and/or further research? Women's childbirth self efficacy can be improved with trainings and appropriate nursing interventions. For this reason, it is important to determine the factors affecting the self-efficacy perception of women. In future studies, the childbirth self-efficacy perceptions of women in different samples (risky pregnancy, disabled pregnant, etc.) should be measured.

An application of data mining algorithms for predicting factors affecting Big Data Analysis adoption readiness in SMEs.

The adoption of Big Data Analysis (BDA) has become popular among firms since it creates evidence for decision-making by managers. However, the adoption of BDA continues to be poor among small and medium enterprises (SMEs). Therefore, this study adopted the Technology-Organization-Environment (TOE) framework to identify the drivers of readiness to adopt BDA among SMEs. Chi-square automatic interaction detection (CHAID), Bayesian network, neural network, and C5.0 algorithms of data mining were utilized to analyze data collected from 240 Vietnamese managers of SMEs. The evaluation model identified the C5.0 algorithm as the best model, with accurate results for the prediction of factors influencing the readiness to adopt BDA among SMEs. The findings revealed management support, data quality, firm size, data security and cost to be the fundamental factors influencing BDA adoption readiness. Moreover, the results identified the service sector as having a higher level of readiness toward the adoption of BDA compared to the manufacturing sector. The findings are imperative for the enhancement of the decision-making process and advancement of comprehension of the determinants of BDA adoption among SMEs by researchers, managers, providers and policymakers.

Determinants of compulsory hospitalisation at admission and in the course of inpatient treatment in people with mental disorders-a retrospective analysis of health records of the four psychiatric hospitals of the city of Cologne.

BACKGROUND: We aimed to identify differences in predictors of involuntary psychiatric hospitalisation depending on whether the inpatient stay was involuntary right from the beginning since admission or changed from voluntary to involuntary in the course of in-patient treatment. METHODS: We conducted an analysis of 1,773 mental health records of all cases treated under the Mental Health Act in the city of Cologne in the year 2011. 79.4% cases were admitted involuntarily and 20.6% were initially admitted on their own will and were detained later during the course of in-patient stay. We compared the clinical, sociodemographic, socioeconomic and environmental socioeconomic data (ESED) of the two groups. Finally, we employed two different machine learning decision-tree algorithms, Chi-squared Automatic Interaction Detection (CHAID) and Random Forest. RESULTS: Most of the investigated variables did not differ and those with significant differences showed consistently low effect sizes. In the CHAID analysis, the first node split was determined by the hospital the patient was treated at. The diagnosis of a psychotic disorder, an affective disorder, age, and previous outpatient treatment as well as the purchasing power per 100 inhabitants in the living area of the patients also played a role in the model. In the Random Forest, age and the treating hospital had the highest impact on the accuracy and decrease in Gini of the model. However, both models achieved a poor balanced accuracy. Overall, the decision-tree analyses did not yield a solid, causally interpretable prediction model. CONCLUSION: Cases with detention at admission and cases with detention in the course of in-patient treatment were largely similar in respect to the investigated variables. Our findings give no indication for possible differential preventive measures against coercion for the two subgroups. There is no need or rationale to differentiate the two subgroups in future studies.

Developing a simple and practical decision model to predict the risk of incident type 2 diabetes among the general population: The Di@bet.es Study.

AIMS: To develop a simple multivariate predictor model of incident type 2 diabetes in general population. METHODS: Participants were recruited from the Spanish Di@bet.es cohort study with 2570 subjects meeting all criteria to be included in the at-risk sample studied here. Information was collected using an interviewer-administered structured questionnaire, followed by physical and clinical examination. CHAID algorithm, which collects the information of individuals with and without type 2 diabetes, was used to develop a decision tree based type 2 diabetes prediction model. RESULTS: 156 individuals were identified as having developed type 2 diabetes (6.5% incidence). Fasting plasma glucose (FPG) at the beginning of the study was the main predictive variable for incident type 2 diabetes: FPG ≤ 92 mg/dL (ref.), 92-106 mg/dL (OR = 3.76, 95%CI = 2.36-6.00), > 106 mg/dL (OR = 13.21; 8.26-21.12). More than 25% of subjects starting follow-up with FPG levels > 106 mg/dL developed type 2 diabetes. When FPG <106 mg/dL, other variables (fasting triglycerides (FTGs), BMI or age) were needed. For levels ≤ 92 mg/dL, higher FTGs levels increased risk of incident type 2 diabetes (FTGs > 180 mg/dL, OR = 14.57; 4.89-43.40) compared with the group of FTGs ≤ 97 mg/dL (FTGs  = 97-180 mg/dL, OR = 3.12; 1.05-9.24). This model correctly classified 93.5% of individuals. CONCLUSIONS: The type 2 diabetes prediction model is based on FTGs, FPG, age, gender, and BMI values. Utilizing commonly available clinical data and a simple blood test, a simple tree diagram helps identify subjects at risk of developing type 2 diabetes, even in apparently low risk subjects with normal FPG.

Prediction of the live weight at breeding age from morphological measurements taken at weaning in indigenous Honamli kids using data mining algorithms.

The purpose of this study was to predict live weight at breeding age (LW) based on weaning morphological traits, which birth weight (BW), weaning weight (WW), withers height (WH), back height (BH), rump height (RH), chest depth (CD), body length (BL), tail length (TL), chest girth (CG), leg girth (LG), front shark circumference (FSC), head length (HL), head width (HW), nose length (NL), ear length (EL), and ear width (EW). For this purpose, measurements were taken from 84 Honamli kids born in 2018. The study also included sex, birth type (BT), and ear type as the nominal predictors. For this purpose, two MARS (Multivariate Adaptive Regression Splines), which are interaction (MARS2) and non-interaction (MARS1), and based-tree algorithms, such as CART (Classification and Regression Tree), CHAID (Chi-Square Automatic Interaction Detector), and Exhaustive CHAID, were used by cross-validation 5 and compared with each other considering the predictive performance by taking into account nine predictive performances criteria. LW has a significantly positive and high linear relationship with WH (0.770), BH (0.770), RH (0.750), BL (0.750), and CG (0.770), respectively (p < 0.01). According to these criteria, second-order interaction MARS2 model had the best performance among all data mining algorithms. Also, the CHAID algorithm was the best predictor of LW among regression tree-based algorithms. The CHAID algorithm predicted that the Honamli goat with 14.426 < WW < 15.575 kg and HW > 16.464 cm had the heaviest LW at 56.268 kg. The MARS2 model showed that the heaviest LW could be produced by WW > 16.10 kg, HW > 17 cm, Sex-Male × BL > 60 cm, WW × BL < 50 cm, BT-twin × WW < 15.60 kg, BL > 50 cm × CG > 62.4 cm and male goats. Also, CHAID and MARS2 algorithms explain 92.00% and 94.50% of the variation in LW, respectively. According to the results, it can be concluded that the CHAID and MARS algorithms used in the prediction of LW at breeding age could give an idea to reveal the breed standards examined for breeding purposes. While determining that there are important statistical methods in defining body characteristics at weaning in a complex way, the body characteristics determined by these models can be used as indirect selection criteria.