Exhaustive CHAID Research Articles

Firearm Access and Socio-Structural Factors Related to Suicidality Among Youth With Diverse Sexual, Gender, and Racial Identities

Objective Elevated rates of suicidal ideation (SI) and suicide attempts (SA) among youth, particularly multiply marginalized youth, are occurring in the context of youths’ access to household firearms. Research examining how perceived access to firearms is related to SI and SA among youth with marginalized identities is limited and often neglects to consider intersectionality. This study explored how intersecting social identities and positions, access to firearms, and socio-structural factors were associated with SI and SA for youth. Method The analytic sample (N = 17,794) included 7-12th grade students who participated in the 2021 Dane County Youth Assessment. Exhaustive CHAID – a decision tree matrix approach – examined all possible combinations of self-reported sociodemographic characteristics (gender identity, sexual identity, racial identity, grade, and firearm access) and socio-structural variables (bias-based bullying, school belongingness, and social pressure) to predict mutually exclusive groups of youth based on past-year SI and SA. Results SI and SA was most prevalent among intersectional groups with multiply marginalized identities and access to firearms. Socio-structural factors, including bias-based bullying victimization, lack of school belongingness, and social pressure, were characteristic of groups with higher prevalence of SI and SA. Conclusions While the marginalized youth in this sample have lower access to firearms, the prevalence of SI and SA was highest among multiply marginalized youth who reported access to firearms in the context of bias-based bullying, social pressure, and a lack of school belongingness. Youth suicide prevention efforts would be strengthened by policies that address firearm access and improve the school environment.

Comparison of Predictive Performance of Data Mining Algorithms in Predicting Tomato Yield with the A Case Study in Igdir

Among the vegetable species in the world, the plant with the most cultivation area is tomato. Increasing tomato yield is important in terms of contributing more to the world economy, producer’s income and human health. With the advancement in software technologies, the importance of data mining algorithms is increasing due to the fact that these algorithms can produce more sophisticated solutions for regression and classification problems. Determining the factors affecting tomato yield and comparing different data mining algorithms on prediction of tomato yield are the purpose of this study. For this purpose, survey study was conducted with the 105 farmers, selected by Simple Random Sampling Method in Igdir province in 2016. Different data mining algorithms including Classification and Regression Tree, Exhaustive CHAID, Chi-Square Automatic Interaction Detector, Artificial Neural Network Algorithm, Multivariate Adaptive Regression Splines and General Linear Model were developed and compared their predictive performance. MARS decision tree has been able to build a model with greatest predictive accuracy, and the others are respectively ANN, GLM, CART, CHAID and Exhaustive CHAID. In the MARS model, number of irrigation , amount of chemical fertilizer , age of farmer , number of seedlings , education level , soil analysis status , sowing region were found statistically significant (P˂0.05). Preferring the MARS model could give an opportunity to detect factors affecting tomato yield and their interactions with higher accuracy. Moreover, results can be easily interpreted and the rules are understandable.

Examining the Influence of Narcissism and Some Demographic Variables on Online Shopping Addiction via the Exhaustive CHAID Method

AbstractThe literature posits that narcissism may theoretically influence online shopping addiction, with materialism being regarded as a consequence of narcissistic tendencies that subsequently exacerbate shopping addiction. Additionally, it is stated in the literature that age and gender are also associated with online shopping addiction. So, the present study aims to examine the effect of narcissism, age, and gender on online shopping addiction using the exhaustive CHAID analysis. In the research, which was designed as a survey and correlational study, data were collected from 1010 adults using the online shopping addiction scale and narcissism scale. The data obtained were analyzed with descriptive statistics and the exhaustive CHAID method. According to the analysis results, the participant group’s level of online shopping addiction was low, and their level of narcissism was moderate. In line with the results of the exhaustive CHAID analysis, the variables that most related to online shopping addiction were age, gender, and narcissism level. According to the analysis results, online shopping addiction decreases with advancing age. Women in younger age groups are more addicted to online shopping than men. Moreover, the increase in the level of narcissism is a condition that elevates the online shopping addiction of women in younger groups. It is reported in the literature that narcissism is more common in men and younger individuals. However, one of the most important study results is that, in this study, individuals with online shopping addiction are mostly relatively young female participants with high levels of narcissism.

Prediction of The Factors Affecting Watermelon (Citrullus lanatus (Thunb.) Matsum. & Nakai) Yield Using Data Mining

The aim of this study was to evaluate the factors of affecting watermelon yield in Diyarbakır province. The data was obtained from surveying of 80 watermelon farmers in Diyarbakır province, Turkey by Simple Random Sampling Method using the Chi-square automatic interaction detector (EXHAUSTIVE CHAID) algorithm of the Data Mining Regression Tree methods. In the model created, the dependent variable was WY (watermelon yield), and the independent variables were determined as R (region), AF (age of farmer), EL (education level), CA (cultivation are), FD (fertilization date), FA (amount of fertilization), DS (date of spraying), AS (amount of spraying), NI (number of irrigation), IT (irrigation time), AN (anchor number), HT (harvest time). As a result of the study, the factors that significantly affect the yield of watermelon; AN, NI, HT, CA, R has been determined. An average of 4488.9 kg watermelon yield per decare was obtained and the number of hoes was the variable that most affected the watermelon yield. As a result in order to get a higher yield per unit area, watermelon producers should anchor number more than 4 times, irrigate 5 to 6 times at less than 2 hours, and apply fertilizer in May. In addition, Çermik, Eğil, Yenişehir and Bismil were determined as more suitable regions for watermelon production.

West African Dwarf goat populations of tropical rainforest zone: application of multivariate analysis tools on their characterization using morpho-biometric traits.

This research was carried out to characterize the West African Dwarf (WAD) goats using multivariate approach based on their morpho-biometric traits in the tropical rainforest zone of Nigeria. Data on four physical qualitative traits, six morphology indicators, and nine morphological characters were generated from 279 goats. Descriptive statistics, non-parametric analysis and one-way approach of ANOVA with post hoc tests, discriminant analysis (canonical), principal component analysis (categorical), and regression tree analysis were employed to discern the effects of location and sex on the parameters, as well as characterize the goats. The frequencies of the qualitative traits show black coat color predominated (60.2%) over other coat colors, plain color predominated (75.3%) over other color patterns, straight horns (38.1%) predominate over other horn shape, and beard presence (66.7%) predominated over non-beard presence among the goats in terms of locations and sexes. Location and age were significantly affected on the biometric characters (p<0.05 and p<0.001) with goats from Enugu having higher values than other states, while only head length (HL) was affected significantly by sex (p< 0.05). The morphological indicators were not significantly affected by location and sex (p>0.05 and p>0.001), while age was significant. The discriminant analysis revealed a sparse, non-intermingling populations for the physical traits, biometric characters, and body indices implying they are different distinct populations from each other. Generally, the goat populations could be characterized using heart girth (HG), rump height (RH), and body weight (BW) due to the principal components, while the CHAID and Exhaustive CHAID algorithm analysis revealed BW and HL as the genetic classifiers of the WAD goats, both in terms of locations. Basically, the goats from the 3 locations appeared to be more homogeneous in nature, implying that a deployment of specific complimentary genomics work targeted at future selection and breeding programs to improve their productivity in the tropical rainforest of Nigeria is necessary.

Predictive data mining models in the tests of propelling charges

In the article, in the introduction, the concept of predictive data mining models and was defined and the purpose of the article was specified. Then, the method of building predic-tive models was characterized and the elements of ammunition were indicated, the test results of which were prepared for the building of models, and the types of ammunition in which the propellant charge is present were indicated. The results of building four data mining models are presented. Predictive models for C&amp;RT, CHAID and exhaustive CHAID decision trees were designed and built. The fourth model analyzed was the SANN model, i.e. the model of neural networks. For each of the tree models, a schema of the designed tree, the rate of false predictions and the parameters of goodness of fit of the built models are shown. For the SANN model, the parameters of the selected neural network were addi-tionally characterized. An analysis of the built models was made and, based on the ob-tained results, the best designed predictive data mining model was indicated. At the end, the graphical form of the workspace predefined by the GC Advanced Comprehensive Classifiers project is shown.

P18.10.B Natural history of meningiomas - a serial volumetric analysis of 240 tumors

Abstract Background The management of asymptomatic intracranial meningiomas is controversial. Through the assessment of growth predictors, we aimed to create the basis for practicable clinical pathways for the management of these tumors. Material and Methods We volumetrically analyzed meningiomas radiologically diagnosed at our institution between 2003 and 2015. For this purpose, we used exclusively thin-layered MR images (i.e. ≤ 2mm slice thickness). The primary endpoint was tumor growth defined as a 14.35% increase in tumor volume. We identified predictive clinical and radiological characteristics and used the significant variables from a multivariable regression model to construct a decision tree based on the exhaustive Chi-squared Automatic Interaction Detection (exhaustive CHAID) algorithm. Results Of 240 meningiomas, 159 (66.3%) demonstrated growth during a mean observation period of 46.9 months. On multivariable logistic regression analysis, older age (OR=0.979 (0.958-1.000), p=0.048) and presence of calcification (OR=0.442 (0.224-0.872), p=0.019) had a negative predictive value for tumor growth, while T2-signal iso-/hyperintensity (OR=4.415 (2.056-9.479), p&amp;lt;0.001) had a positive predictive value. A decision tree model yielded three growth risk groups based on T2-signal intensity and presence of calcifications with a proportion of growing tumors of 34.1% in the low risk group, 60.0% in the intermediate risk group and 80.2% in the high risk group. Median tumor volume doubling time (Td) was 185.7 months in the low risk, 100.1 months in the intermediate risk and 51.7 months in the high risk group (p&amp;lt;0.001). While 0% of meningiomas in the low and intermediate risk group had a Td of ≤12 months, 8.9% in the high risk group did so (p=0.021). Conclusion Most meningiomas demonstrated growth during follow-up. The presence or absence of calcifications and the signal intensity on T2-weighted imaging allow a practical and simple stratification of meningiomas into low, intermediate and high risk tumors. Small tumors in the low or intermediate risk categories can be monitored with longer follow-up intervals, whereas in the high risk category proactive management decisions can be justified.

Prediction Performance Comparison of Risk Management and Control Mode in Regional Sites Based on Decision Tree and Neural Network.

The traditional risk management and control mode (RMCM) in regional sites has the defects of low efficiency, high cost, and lack of systematism. Trying to resolve these defects and explore the application possibility of machine learning, a characteristic dataset for RMCM in regional sites was established. Three decision tree (DT) algorithms (CHAID, EXHAUSTIVE CHAID, and CART) and two artificial neural network (ANN) algorithms [back propagation (BP) and radial basis function (RBF)] were implemented to predict RMCM in regional sites. The results showed that in the aspects of accuracy (ACC), precision (PRE), recall ratio (REC), and F1 value, CART–DT was superior to CHAID–DT and EXHAUSTIVE CHAID–DT (E-CHAID–DT); and BP–ANN was superior to RBF–ANN. However, CART–DT was inferior to BP–ANN in ACC, PRE, REC, and F1 value. BP–ANN model is good at non-linear mapping, and it has a flexible network structure and a low risk of over-fitting. The case study of a typical county demonstration area confirmed the extensibility of the method, and the method has great potential in RMCM prediction in regional sites in the future.

Linking wild turkey hen movement data to nesting behavior

AbstractMonitoring of wild turkey (Meleagris gallopavo) females to obtain representative measures of apparent nesting and nest survival rates has long been an important component of wild turkey management. In the interest of improving estimates of apparent nesting and nest survival rates, we assessed the feasibility of inferring reproductive behavior of wild turkeys from remotely acquired movement data. We ascertained detailed nesting stage chronologies using Global Positioning System (GPS) data downloaded from 44 females fitted with transmitters that had both a very high frequency (VHF) beacon and a GPS logger. Then we tracked females using VHF to confirm nest locations and nest fate in north and north‐central Florida. Using twice hourly GPS derived locations, we evaluated 3 predictive machine learning models (Chi‐squared automatic interactive detection [CHAID], exhaustive CHAID, and multivariate adaptive regression splines [MARSpline]) for their ability to accurately classify nesting behaviors (laying and incubating stages) that were confirmed via visual inspection of all potential nest sites. Incubation stage was readily identifiable by the spatial signatures present in each female's GPS‐based movement track across all models (up to 92% true positive and as low as 8% false positives in exhaustive CHAID models), whereas the laying stage was more difficult to identify (up to 65% true positive and as low as 35% false positives in exhaustive CHAID models). Daily averages of movement metrics generally achieved better performance in predictive models than data sets with more frequent sampling (i.e., 30‐minute daytime locations). Our results indicated that exhaustive CHAID models performed best in identifying nesting stages, using movement metrics from GPS data. With continued development, these models may facilitate and improve large‐scale wild turkey monitoring.

Use of Multivariate Adaptive Regression Splines, Classification Tree and Roc Curve in Diagnosis of Subclinical Mastitis in Dairy Cattle

Subclinical mastitis is one of the most significant diseases that cause economic losses in dairy cattle farming. This investigation was conducted on 112 head Holstein Friesian cows in order to reveal relationship between subclinical mastitis and electrical conductivity milk composition and milk quality. In the study, CMT (California Mastitis Test) and CSCC (Classified Somatic Cell Count) used in diagnosis of subclinical mastitis were used as a binary response variable i.e. healthy and unhealthy. Potential predictors included here were lactation number, days in milk (DIM), L, a, b, H, C, milk fat, milk protein, lactose, milk freezing point, SNF, density, solids, pH and electrical conductivity. CART, CHAID, Exhaustive CHAID, QUEST and multivariate adaptive regression splines (MARS) were used as data mining algorithms that help to make an accurate decision about detecting influential factors increasing risk of subclinical mastitis.In conclusion, better classification performances of CART and MARS data mining algorithms were determined compared with those of remaining algorithms in order to correctly discriminate healthy and unhealthy cows.

Impact of Social Capital on Residents' Choice of Medical Institutions: A Study Based on Classification Tree Model and Logistic Regression

To explore the influence of social capital on the local residents' choice of medical institutions and to provide a reference basis for promoting diagnosis and treatment services available at different tiers. A classification tree model was established using the exhaustive chi-square automatic interaction detection (Exhaustive CHAID) method to screen for factors influencing the residents' choice of medical institutions, and a logistic regression model was used to quantitatively analyze the interaction effect of the influencing factors. The classification tree model showed that there were four layers and eight terminal nodes, identifying a total of six influencing factors, including individual social capital, self-reported physical health, education, community social capital, chronic disease prevalence, and self-reported mental health. Logistic regression analysis showed that education (odds ratio [OR]=0.660, 95% confidence interval [CI]: 0.502-0.869), community social capital (OR=0.746, 95% CI: 0.589-0.943), and individual social capital (OR=0.405, 95% CI: 0.287-0.572) (P<0.001) had an impact on residents' choice of medical institution. There was an interaction between individual social capital and self-reported physical health on residents' choice of medical institution (OR=1.872, 95% CI: 1.180-2.969, P<0.05). Interventions in terms of social capital factors should be considered in order to promote the rational use of medical resources.

Modelling of Energy Storage System from Photoelectric Conversion in a Phase Change Battery

The essence of the research was to model the actual energy storage system obtained from photoelectric conversion in a phase change accumulator operating in a foil tunnel. The scope of the work covered the construction of four partial models, i.e., electricity yield from solar radiation conversion for three types of photovoltaic cells (mono- and polycrystalline and CIGS), energy storage in a PCM battery, heat losses in a PCM battery and energy collection from photoelectric conversion in PCM battery. Their construction was based on modelling methods selected on the basis of literature review and previous analyses, i.e., artificial neural networks (ANN), random forest (RF), enhanced regression trees (BRT), MARSplines (MARS), standard multiple regression (SMR), standard C&amp;RT regression trees (CRT), exhaustive CHAID for regression (CHAID). Based on the analysis of the error values (APE, MAPE, ΔESRt), the best quality models were selected and used in the further part of the work. Based on the developed models, a simulation of the influence of the size of the photovoltaic power plant and the type of cells on the process of storing energy from photoelectric conversion in a PCM battery was carried out. For the battery under study, a PV power output of 9 kWp for mono and polycrystalline panels and 13 kWp for CIGS panels is recommended for reasons of energy storage efficiency. The obtained results made it possible to develop a model determining the amount of energy stored in a phase change battery depending on the power of a photovoltaic power plant and variable solar conditions. In order to store the greatest amount of energy, we should choose a source with a capacity to produce at least 70 kWh of electricity per day. In the final stage of the work, the indicators of solar radiation energy storage in the tested phase change accumulator were determined. For the battery tested, the solar energy storage efficiency can reach 12–13% for mono and polycrystalline panels and less than 7% for CIGS panels.

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Predicting sexual offenders using exhaustive CHAID techniques on victim's age

Relationship among egg quality traits in Japanese quails and prediction of egg weight and color using data mining algorithms.

The present paper aims to predict egg weight from some egg quality characteristics (egg weight, yolk weight, albumen weight, shell thickness, shell weight, shape index, albumen index, yolk index, yolk scale, and Haugh unit) in Japanese quails and to classify 94 eggs collected in regards to their shell colors. In the present study, CART and MARS data mining algorithms were assessed in the prediction of egg weight of the quails with the content of detecting egg quality standards of the studied quail genotypes for breeding and marketing strategies. In the classification of the collected quail eggs on their shell colors, classification performances of CART, CHAID, exhaustive CHAID, and QUEST algorithms were measured. Among those, CART was selected as the best classification algorithm according to eggshell color. The highest significant correlations were obtained for egg weight-yolk weight (0.740) and egg weight-albumen weight (0.735), respectively, in considering egg internal and external quality traits in quails. CART algorithm more accurately classified all eggshell colors compared with other algorithms. MARS showed much better predictive performance than CART that produced 0.850 Rsq and 0.728 cross-validation Rsq for prediction of egg weight in quails. In conclusion, the obtained results revealed that data mining algorithms may be useful references in practice for quail breeders in the development of new selection strategies and characterization of the studied animal materials.

Go/No-Go Decision Model for Owners Using Exhaustive CHAID and QUEST Decision Tree Algorithms

Go/no-go execution decisions are one of the most important strategic decisions for owners during the early stages of construction projects. Restructuring the process of decision-making during these early stages may have sustainable results in the long run. The purpose of this paper is to establish proper go/no-go decision-tree models for owners. The decision-tree models were developed using Exhaustive Chi-square Automatic Interaction Detector (Exhaustive CHAID) and Quick, Unbiased, Efficient Statistical Tree (QUEST) algorithms. Twenty-three go/no-go key factors were collected through an extensive literature review. These factors were divided into four main risk categories: organizational, project/technical, legal, and financial/economic. In a questionnaire distributed among the construction professionals, the go/no-go variables were asked to be ranked according to their perceived significance. Split-sample validation was applied for testing and measuring the accuracy of the Exhaustive CHAID and QUEST models. Moreover, Spearman’s rank correlation and analysis of variance (ANOVA) tests were employed to identify the statistical features of the 100 responses received. The result of this study benchmarks the current assessment models and develops a simple and user-friendly decision model for owners. The model is expected to evaluate anticipated risk factors in the project and reduce the level of uncertainty. The Exhaustive CHAID and QUEST models are validated by a case study. This paper contributes to the current body of knowledge by identifying the factors that have the biggest effect on an owner’s decision and introducing Exhaustive CHAID and QUEST decision-tree models for go/no-go decisions for the first time, to the best of the authors’ knowledge. From the “sustainability” viewpoint, this study is significant since the decisions of the owner, based on a rigorous model, will yield sustainable and efficient projects.

Prediction of serum biochemistry of pigs at pre- and post-weaning stages of growth using regression tree methods

This study was aimed at evaluating the effects of breed, sex and their interactions on aspects of haematology, serum biochemistry and morphometry of healthy pigs at pre- and post-weaning stages of growth (4 and 12 weeks respectively). It was also aimed at predicting the serum biochemical parameters (SBP) that are biomarkers for growth using regression tree method (RTM). Seventy-one crossbred (71 CB) pigs (Landrace × Local pigs) and 21 Local (L) pigs were reared from birth to adult stage, with blood samples and morphometric parameters (MP) taken at pre- and post-weaning stages. The CB pigs had higher values for aspartate transaminase (AST) (serum glutamic oxaloacetic transaminase, SGOT) (P < 0.05) than local ones, while the female animals had higher values (P < 0.05) than the males at pre-weaning stage of growth. At post-weaning stage, the CB breeds also had higher AST (SGOT) values (P < 0.05) than the L breeds, while among the sexes, the males had higher values for WBC, L, mean corpuscular haemoglobin and mean corpuscular haemoglobin concentration (P < 0.05) than the female pigs, but the females were higher for N and AST SGOT (P < 0.05) than the males respectively. However, the prediction of SBP using Exhaustive CHAID algorithm of RTM returned 100% prediction in terms of a single terminal node from all the morphometric parameters at both stages of growth. The terminal nodes of the RTM produced mean values of 34.57 UI/L for alkaline phosphate, 29.25 UI/L for AST and 60.27 UI/L for alanine transaminase (ALT) respectively. This implies that the prediction of ALKPOS, AST (SGOT) and ALT (serum glutamic pyruvic transaminase) using the 5 morphometric traits measured is not significant as no single node was linked to them. Although the node values reported for them are within the range of 0–40 IU/L for healthy pigs, the values for ALT are above this range. Therefore, individual contributions from the morphometric traits could not predict any SBP as the model returned zero node.

Patterns of elderly people's long-term care risks and determinants: A methodological study in a Japanese city.

Existing analysis methods about the relationships between the long-term care (LTC) risks and determinants of the elderly often experience issues of independence, linearity and homogeneity. Such methodological issues are particularly problematic when the relationships are complicated, making the prioritization of LTC interventions to groups with different care needs difficult. To overcome these shortcomings, this study made an initial attempt to identify representative patterns of elderly people's LTC risks associated with different determinants, from a methodological perspective. A data mining approach (Exhaustive CHAID) is applied to data collected from 26 357 elderly people in a Japanese city in 2016. It can automatically segment the samples into a tree structure that accommodates nonlinear, heterogeneous and sequential relationships, without caring about issues of independence across determinants. In total, 16 representative patterns are derived, among which four patterns are considerably riskier (the unhealthy percentage >10.0%), and five are moderately riskier (the unhealthy percentage is 5.0%-10.0%). Age is the most important determinant, followed by locomotor function, cognitive function, homebound, gender and residential location, in that order, which all show heterogeneous associations with the risk patterns. The Exhaustive CHAID is suitable to derive scientifically-sound insights into an early screening of risk factors for pre-frail groups and tailor-made preventive LTC interventions for frail groups in a flexible and objective way. Geriatr Gerontol Int 2020; 20: 674-679.

Serving Patterns of Women's Badminton Medalists in the Rio 2016 Olympic Games.

The aim of the present study was to describe and identify the serving performance profiles of medalists during an elite women’s badminton tournament taking notational and temporal variables into account. The sample was composed of the 14 matches (n = 1,052 rallies) played by the three medalists during the 2016 women’s singles Olympic Games badminton event (Rio, Brazil). The independent variable studied was serving player (medalist/opponent); while the dependent variables were related to notational analysis: serve type, set, and point won by the server/receiver; and the time-related variables: number of strokes per rally, rally time, rest time, and frequency of strokes. The main results showed that: (i) temporal parameters were similar for total match duration but shorter for rally time, and longer for rest time and with more strokes per rally than found in previous research; (ii) the serve effectiveness showed neutral values when analyzing serving by all the players, medalists, and opponents (around 50%); (iii) the two-step cluster analysis identified how successful players used the serve when playing short rallies with backhand short and flick serves (cluster 1), and forehand long serves (cluster 2); and during long rallies with the use of the backhand short serve, forehand short serve and forehand long serve (cluster 3). On the other hand, medalists and their opponents used forehand long serves during set 1 with durations of 8.80 s (cluster 5); and the opponents showed an independent performance using the forehand short serve during sets 1 and 2 (cluster 4); and (iv) the classification tree analysis (Exhaustive CHAID) identified the importance of different serving patterns with the gold medal player using more backhand and forehand flick serves, and the main use of backhand short serves during sets 1 and 2 in all the tournament stages. The bronze medalist used more forehand long serves during all sets, and the silver medalist showed a mixed performance of serves using the forehand short serve, the backhand short serve and the forehand long serve. The current findings may help coaches and players to manage different serving and playing patterns during training and matches according to the serve and rally requirements.

Risk factors for febrile urinary tract infections in the first year after pediatric renal transplantation.

Urinary tract infection is the most common infectious complication following kidney transplant. Anatomic abnormalities, bladder dysfunction, a positive history of febrile urinary tract infection, and recipient age are reported risk factors. The aim of this study was to determine the risk factors for fUTI, which necessitated hospitalization in the first year after renal transplantation in our pediatric transplant population. A retrospective review of 195 pediatric patients who underwent kidney transplant between 2008 and 2017 from a single institution was performed. All patients admitted to the hospital with fUTI were marked for further analyses. The risk factors including age, gender, dialysis type, history of urologic disorders, and preoperative proteinuria for fUTI in the first year after kidney transplantation and graft survivals were investigated. Independent-sample t test and chi-square tests were used for univariate analysis. Exhaustive CHAID algorithm was used for multivariate analysis. The data of 115 male and 80 female patients were retracted. The mean ages of our cohort for males and females were 9.5±5.1 and 10±4.8years, respectively. The age of the patients at transplant and their gender were found to be a statistically significant risk factors for developing fUTIs. Multivariate analysis showed that fUTI was common in female patients and a subgroup of male patients who had preoperative proteinuria, but no neurogenic bladder had higher risk compared with male patients without proteinuria. Patient surveillance and antibiotic prophylaxis algorithms can be developed to prevent febrile urinary tract infections seen after pediatric kidney transplantation in risky population.