Chi-squared Automatic Interaction Detection Method Research Articles

Evidence from the large VALIGA cohort validates the subclassification of focal segmental glomerulosclerosis in IgA nephropathy

Evidence from the Oxford IgA nephropathy (IgAN) cohort supports the clinical value of subclassifying focal segmental glomerulosclerosis lesions (S1). Using the larger Validation in IgA (VALIGA) study cohort, we investigated the association between podocytopathic changes and higher proteinuria, kidney outcome and response to immunosuppressive therapy. All biopsies were evaluated for glomeruli with segmental capillary occlusion by matrix ("not otherwise specified", NOS lesion), simple capsular adhesion without capillary occlusion (Adh), tip lesions, and podocyte hypertrophy (PH). S1 required a NOS lesion and/or Adh. A Chi-Squared Automatic Interaction Detection method was used to identify subgroups of FSGS lesions associated with distinctive proteinuria at biopsy and assessed survival from a combined event (kidney failure or 50% decline in estimated glomerular filtration rate). Finally, we evaluated within each subgroup if immunosuppression was associated with a favorable outcome using propensity analysis. In 1147 patients, S1 was found in 70% of biopsies. Subclassification found NOS lesions in 44%, Adh in 59%, PH in 13%, and tip lesions in 3%, with much overlap. Four subgroups were identified with progressively higher proteinuria: from lowest, S1 without NOS, S1 with NOS but without Adh/PH, to highest, S1 with NOS and Adh but without PH, and S1 with NOS and PH. These four subgroups showed progressively worse kidney survival. Immunosuppression was associated with a better outcome only in the two highest proteinuria subgroups. Propensity analysis in these two groups, adjusted for clinical and pathological findings, found a significant time-dependent hazard of combined outcome with corticosteroids. Podocyte hypertrophy and glomeruli with simple adhesions appeared to reflect active lesions associated with a response to corticosteroids, while other S1 lesions defined chronicity. Thus, our findings support subclassifying S1 lesions in IgAN.

Comparison of Error Prediction Methods in Claassification Modeling with CHAID Methods for Balanced Data

Chi-Squared Automatic Interaction Detection (CHAID) is an exploratory method for classifying data by building classification trees. The classification result are displayed in the form of a tree diagram model. After the model is formed, it is necessary to calculate the accuracy of the model. The goal is to see the performance of the model. The accuracy of this model can be determined by calculating the level of prediction error in the model. The error rate prediction method works by dividing data into training data and testing data. There are three methods in the error rate prediction method, such as Leave one out cross validation (LOOCV), Hold out, and k-fold cross validation. These methods have different performance in dividing data into training data and test data, so that each method has advantages and disadvantages. Therefore, a comparison of the three error rate prediction methods was carried out with the aim of determining the appropriate method for the CHAID. This research is included in experimental research and uses simulation data from data generation results in RStudio. This comparison is carried out by considering several factors, namely the marginal probability matrix and different correlations. The comparison results will be observed using a boxplot by looking at the median error rate and lowest variance. This research found that k-fold cross validation is the most suitable error rate prediction method applied to the CHAID method for balanced data.

Improved Chi Square Automatic Interaction Detection on Students Discontinuation to Secondary School

Improved Chi Square Automatic Interaction Detection (CHAID) with bias correction is the developmentof the CHAID method by relying on Tschuprow’s T test calculations with bias correction in the processof forming a classification tree. This study aims to obtain a classification of factors which influencestudents for not continuing their education from junior high school or equivalent to high school orequivalent. The results obtained in the classification tree produce nine classifications. Based on theresults of the classification tree, the classification of students who do not continue their education tohigh school or equivalent is: students with disabilities who do not have access to Information andCommunication Technology (ICTs) (0.89); students who work without disability but do not have accessto ICTs (0.73); and students who do not work without disability but do not have access to in ICTs(0.60). Based on the classification obtained the factors which influence students for not continuingtheir education to high school or equivalent are access to ICTs, employment status, and persons withdisabilities. The classification accuracy of the results uses the Improved-CHAID method with biascorrection with a proportion of 80% training data and 20% testing data, namely 72.3033% on trainingdata and an increase of 73.3300% on testing data.

Comparison of Error Rate Prediction Methods in Classification Modeling with the CHAID Method for Imbalanced Data

CHAID (Chi-Square Automatic Interaction Detection) is one of the classification algorithms in the decision tree method. The classification results are displayed in the form of a tree diagram model. After the model is formed, it is necessary to calculate the accuracy of the model. The aims is to see the performance of the model. The accuracy of this model can be done by calculating the predicted error rate in the model. There are three methods, such as Leave one out cross-validation (LOOCV), Hold-out, and K-fold cross-validation. These methods have different performances in dividing data into training and testing data, so each method has advantages and disadvantages. Imbalanced data is data that has a different number of class observations. In the CHAID method, imbalanced data affects the prediction results. When the data is increasingly imbalanced the prediction result will approach the number of minority classes. Therefore, a comparison was made for the three error rate prediction methods to determine the appropriate method for the CHAID method in imbalanced data. This research is included in experimental research and uses simulated data from the results of generating data in RStudio. This comparison was made by considering several factors, for the marginal opportunity matrix, different correlations, and several observation ratios. The results of the comparison will be observed using a boxplot by looking at the median error rate and the lowest variance. This research finds that K-fold cross-validation is the most suitable error rate prediction method applied to the CHAID method for imbalanced data.

Student Satisfaction with Admission and Induction in the UK Private Higher Education Sector

The primary purpose of this study was to evaluate the effectiveness of two crucial components-admission and induction processes concerning student satisfaction in private higher education. The significance of addressing students' learning and support needs in higher education is rising and changing, the focus is on their satisfaction, and it has been extended before and after completing studies. Therefore, the study sought student satisfaction during private higher education's admission and induction processes. Using a quantitative approach, we collected data from 713 undergraduate students at Oxford Business College. For this purpose, a questionnaire was designed comprised of fort questions mainly focusing on admission and induction, including demographic factors. The data was analysed using SPSS version 26. Descriptive statistics and log-linear analysis were used to find significant associations between different variables. Moreover, the variables retained in the model were separately used as the outcome/target variable using a decision tree classifier- CHAID (Chi-square Automatic Interaction Detection) method to find the best feature and attain meaningful insights about the data. The research also examined the overall satisfaction to identify students' needs and measure the level of satisfaction with admission and induction. The study revealed high satisfaction levels in admission and induction; however, specific areas in the induction, both academic and general, where there is a lack of information; as a result, developments could be required to ensure the delivery of best practices in these areas. Keywords: Admission, Induction, Student Satisfaction, Private Higher Education

Impact of the COVID-19 Pandemic on the Work Engagement of UK Active Employees.

The objective of this investigation was to describe the work engagement perceived by UK workers during the COVID-19 pandemic. A descriptive cross-sectional study was conducted. The sample included 1085 participants, aged 18 years and older, living in the UK during the COVID-19 pandemic, who were active workers. Data were collected using an online questionnaire and the UWES-9. They were analysed using descriptive statistics, a t-test for equality of means or ANOVA, and the Chi-squared Automatic Interaction Detection method. The mean value in the UWES-9 was 3.46 (SD = 1.11). Participants with lower satisfaction (21.8%) gave significantly low or very low UWES-9 scores in 58.5% of the cases. Greater work engagement was obtained with more resources and less conflict, risk, and stress. In cases where there had been contact with COVID-19, this was associated with slightly lower levels of work engagement. These results could motivate and guide companies to adopt risk prevention measures and protocols to return to normal working conditions after the initial crisis phase of the pandemic.

Penerapan Metode Klasifikasi Chi-Square Automatic Interaction Detection dan Exhaustive Chi-Square Automatic Interaction Detection

The Chi-Square Automatic Interaction Detection (CHAID) and Exhaustive CHAID methods are nonparametric statistical methods that can be used to classify. CHAID and Exhaustive CHAID were used to determine the significant relationship between the dependent variable and the independent variables based on the chi-square independence test. This study was applied to data on the study period of students of FMIPA UNMUL batch 2014. Based on the CHAID and Exhaustive CHIAD methods, it can be seen that the dependent variable of the study period has a significant relationship with the independent variable, namely the study program and GPA predicate. Where students who graduated on time for the Statistics, Biology and Chemistry study program with a satisfactory GPA predicate of 82 students and with a very satisfactory GPA predicate and cum laude with 46 students. Meanwhile, students who did not graduate on time for the Statistics, Biology and Chemistry study program with an adequate GPA predicate of 5 students, a satisfactory GPA predicate of 41 students, very satisfactory and cum laude with 3 students. Students who graduated on time for the Physics study program were 13 students and those who did not graduate on time were 34 students. The chi-square independence test performed on the CHAID method uses fewer possible categorical pairs than the Exhaustive CHAID method which uses all possible categorical pairs so that it requires a long computational and calculation time.

Psychological Distress Among Occupational Health Professionals During Coronavirus Disease 2019 Pandemic in Spain: Description and Effect of Work Engagement and Work Environment.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on the mental health of hospital health professionals has been widely described, but few studies have focused on occupational health professionals. Therefore, the objective of this study was to assess psychological distress (PD) of occupational health workers and its relationship with their work engagement (WE) and work environment characteristics. A cross-sectional survey was conducted. A sample of 499 nurses and physicians participated in the study. Variables included demographic data, work environment characteristics, work engagement Utrecht Work Engagement Scale (UWES-9) and psychological distress General Health Questionnaire (GHQ-12). The Chi-square Automatic Interaction Detection method was performed for data analysis. Data collection took place via the internet between April 23 and June 24, 2020. A total of 65.53% of the participants had PD, and the total mean score of the UWES-9 scale was 34.80 (SD = 10.69). Workload, conflicts, stressful situations, and less job satisfaction were significantly related to a higher percentage of PD (p < 0.05). Participants with low engagement showed higher levels of PD (76.7%; p < 0.001). The dedication was revealed as the most significant dimension. Interventions aimed at promoting resilience and coping strategies are suggested. WE should be fostered as a preventive measure against PD among occupational health workers. By protecting workers, occupational health departments have a shared responsibility with public health in containing the pandemic. Therefore, it is essential to prevent the psychological impact that this responsibility may have on occupational health workers by implementing prevention measures.

Socio-demographic factors of overweight patients with bronchial asthma associated with receiving counseling on weight loss: population study data

BACKGROUND: Asthma and obesity have a close relationship: obesity is a risk factor for asthma, the link of its pathogenesis, a predictor of heavier flow and the worst control. One of the priorities of non-drug therapy of asthma is the fight against overweight. Preventive counseling allows doctors to teach patients the principles of a healthy lifestyle, including controlling body weight. There is no domestic population-based research that reflects the coverage of overweight persons with asthma of the counseling on weight loss in various socio-demographic groups. Thus our study is actual.&#x0D; AIMS: Determine socio-demographic factors of overweight patients with bronchial asthma living in Russian urban areas associated with receiving counseling on weight loss.&#x0D; MATERIALS AND METHODS: This study was based on a cross-sectional population-based study Know Your Heart (20152018, Arkhangelsk, Novosibirsk, n=4504). For this research, we selected overweight patients with asthma (n=167). We applied the CHAID (Chi-Squared Automatic Interaction Detection) decision tree to identify socio-demographic factors associated with receiving weight loss counseling. CHAID method allows for the automated classification of the sample and the detection of relationships between the predictors and the analyzed outcome.&#x0D; RESULTS: The probability of obtaining counseling on weight loss increased by 1.39 times among retired women with obesity compared with the coverage level of counseling in the studied sample as a whole (61.7%). Among the men, the probability of obtaining counseling on weight loss increased 1.27 times in the presence of obesity. Reducing the likelihood of getting counseling both among women (1.39 times compared with the general indicator) and among men (2.2 times compared with the general indicator) was noted if their weight corresponded to the category of the excess body (BMI 25.029.9).&#x0D; CONCLUSION: 61.7% of the overweight urban population of two regions of Russia with asthma received counseling on weight loss. Groups of overweight patients with asthma, which are statistically significantly less often obtaining counseling on weight loss: persons with BMI 25.029.9, regardless of gender, and non-retired women with BMI 30.0. The decision tree developed by us will allow allergists-immunologists and doctors of related specialities to be wary of patients from the population groups identified in the study where the activity of conducting preventive counseling is reduced. In turn, this will increase the coverage of preventive counseling for patients with bronchial asthma and, as a result, will contribute to improving asthma control.

Decision tree analysis to predict the risk of intracranial haemorrhage after mild traumatic brain injury in patients taking DOACs.

Although the preliminary evidence seems to confirm a lower incidence of post-traumatic bleeding in patients treated with direct oral anticoagulants (DOACs) compared to those on vitamin K antagonists (VKAs), the recommended management of mild traumatic brain injury (MTBI) in patients on DOACs is the same as those on the older VKAs, risking excessive use of CT in the emergency department (ED). To determine which easily identifiable clinical risk factors at the first medical evaluation in the ED may indicate an increased risk of post-traumatic intracranial haemorrhage (ICH) in patients on DOACs with MTBI. Patients on DOACs who were evaluated in the ED for an MTBI from 2016 to 2020 at four centres in Northern Italy were considered. A decision tree analysis using the chi-square automatic interaction detection (CHAID) method was conducted to assess the risk of post-traumatic ICH after an MTBI. Known pre- and post-traumatic clinical risk factors that are easily identifiable at the first medical evaluation in the ED were used as input predictor variables. Among the 1146 patients on DOACs in this study, post-traumatic ICH was present in 6.5% (75/1146). Decision tree analysis using the CHAID method found post-traumatic TLOC, post-traumatic amnesia, major trauma dynamic, previous neurosurgery and evidence of trauma above the clavicles to be the strongest predictors associated with the presence of post-traumatic ICH in patients on DOACs. The absence of a concussion seems to indicate subgroups at very low risk of requiring neurosurgery. The machine-based CHAID model identified distinct prognostic groups of patients with distinct outcomes based on clinical factors. Decision trees can be useful as guides for patient selection and risk stratification.

KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X)

One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that affect bad credit such as gender, marital status, education, employment, income, expenses, home ownership status and the dependent variable is credit status (bad and current). The analysis results show that the binary logistic regression has an accuracy value of 76.38% with an APER of 23.62%, while CHAID has an accuracy value of 93.19% with an APER of 6.81%. The accuracy value of the CHAID method is greater than the binary logistic regression method, while the APER value of the CHAID method is smaller than the binary logistic regression method. So it can be concluded that the CHAID method is better than the binary logistic regression method in classifying bad credit at company X. Keywords: Credit, Classification, Binary Logistic Regression, CHAID.

Prey Fragmentation and Transport in Six Tropical Arboreal Ant Species

Prey fragmentation and transport modalities were studied in Myrmicaria opaciventris, Platythyrea conradti, Cremtogaster sp., Crematogaster clariventris, Tetramorium aculeatum and Oecophylla longinoda, six tropical ant species with varying levels of adaptation to foraging in the tree canopy. Termites and grasshoppers of different sizes were used as prey and deposited on a hunting arena. After prey capture, we investigated the strategy adopted by each ant species to fragment and transport their prey. The data were analyzed with classification tree, using Chi-squared Automatic Interaction Detection method to find the variation in prey fragmentation strategies and transportation modalities between arboreal ant species in relation with prey type and prey size. The results showed that prey fragmentation did not occur systematically after capture and varied between ant species with prey type and prey size. Studied ant species cut their prey either completely into tiny pieces or partially into two or three large pieces before cutting them totally into tiny pieces or not at all. Fragmentation strategy, induced variation in transport modalities including (a) transport of large pieces, each by a single worker; (b) transport of large pieces, each, by a group of workers; (c) transport of tiny pieces after total cutting, and (c) transport of entire prey in the nest without fragmentation. Prey fragmentation and transport modalities varied between ant species in relation with their level of adaptation to arboreal area.

Blood acetylcholinesterase activity is associated with increased 10 year all-cause mortality following coronary angiography

Background and aimsParasympathetic dysfunction is associated with increased risk for major adverse cardiovascular events (MACE). However, clinically validated biomarkers that reflect parasympathetic activity are not yet available. We sought to assess the ability of serum cholinesterase activity to predict long term survival in patients undergoing coronary angiography. MethodsWe prospectively followed 1002 consecutive patients undergoing clinically indicated coronary angiography (acute coronary syndrome or stable angina). We measured blood acetylcholinesterase (AChE) activity using the acetylcholine analog acetylthiocholine. Mortality rates were determined up to 10 years of follow-up. We divided our cohort into 3 groups with low, intermediate and high AChE activity by a Chi-square automatic interaction detection method (CHAID). ResultsPatients with lower than cutoff levels of AChE (<300 nmol/min/ml) had higher mortality rates over 10 years of follow-up, after adjusting for conventional risk factors, biomarkers, clinical indication, and use of medications (HR = 1.6, 95% CI 1.1–2.5, p = 0.02). Patients with intermediate levels of AChE (300–582 nmol/min/ml) were also at increased risk for death (HR = 1.4, 95% CI 1.1–1.9, p = 0.02). AChE was inversely correlated with C-reactive protein, troponin I, fibrinogen and neutrophil/lymphocyte ratio levels. ConclusionsPatients presenting for coronary angiography with low levels of serum AChE activity are at increased risk for death during long term follow-up.

The dangerous and contradictory prognostic significance of PVR

AimsIn 2019, pulmonary vascular resistance (PVR) < 3WU was adopted to stratify patients at low risk in pulmonary hypertension due to left heart disease (PH‐LHD) as well those with isolated PH‐LHD. We sought to evaluate whether supervised machine learning with decision tree analysis, which provides more information than Cox Proportional analysis by forming a hierarchy of multiple covariates, confirms this risk stratification.Methods and resultsTwo hundred two consecutive patients (mean age: 69 ± 11 years, female: 42%) with mean pulmonary artery pressure ≥ 20 mmHg and wedge pressure > 15 mmHg were recruited. Transpulmonary pressure gradient ⩾̸ 12 mmHg, PVR ⩾̸ 3WU, diastolic pressure gradient ⩾̸ 7 mmHg, pulmonary arterial capacitance < 1.1 mL/mmHg, tricuspid annular plane systolic excursion (TAPSE) < 16 mm, peak systolic tissue Doppler velocity < 10 cm/s, right ventricular end‐diastolic area ⩾̸ 25 cm2 were the seven categorical values entered into the model due to their prognostic significance in PH. We used the chi‐squared automatic interaction detection method to predict mortality. Each node and branch were compared using survival analysis at 6‐year follow‐up. Mean pulmonary artery pressure, wedge pressure, cardiac index, and PVR were 40.3 ± 10.0 mmHg, 22.3 ± 7.1 mmHg, 2.9 ± 0.8 L/min/m2, and 3.6 ± 2.1WU, respectively. Among the seven dichotomous, TAPSE was first selected following by PVR. Compared with patients with PVR < 3WU and TAPSE ⩾̸ 16 mm, patients with PVR ⩾̸ 3WU and TAPSE ⩾̸ 16 mm, or patients with PVR ⩾̸ 3WU and TAPSE<16 mm had significantly increased mortality, HR = 3.0, 95% CI = [1.4–6.4], P = 0.006 and HR = 3.3, 95% CI = [1.6–6.9], P = 0.002, respectively, while patients with PVR < 3WU and TAPSE < 16 mm exhibited the worst prognosis, HR = 7.2, 95% CI = [3.3–15.9], P = 0.0001.ConclusionsUsed for solving regression and classification problems, decision tree analysis confirms that PVR and TAPSE have to be analysed together in PH‐LHD and revealed the dangerous and contradictory prognostic significance of PVR < 3WU when TAPSE<16 mm.

Identification of the decline in learning outcomes in statistics courses using the chi-squared automatic interaction detection method

The decline in learning outcomes in Statistics is a problem that must be immediately solved. Many factors affect learning outcomes such as; intelligence, interest, talent, motivation, learning patterns, learning resources, and so on. The data used in this study are secondary data from TU-FIPPS and BAAK which consists of data on final grades of Statistics students of the Economic Education Study Program, Indraprasta PGRI University. In addition, researchers also use primary data collected through surveys. Data analysis using the Chi-squared Automatic Interaction Detection (CHAID) method. Based on the results of this study, explanatory variables that affect the decline in student learning outcomes Statistics are variables of motivation and group learning frequency. The explanatory variable which has the strongest influence with the decrease in student learning outcomes Statistics is motivation. The value of the accuracy of student classifications produced using CHAID analysis of eighty-five point one percent. The classification tree model of CHAID analysis in this study is better and more accurate for estimating the decline in learning outcomes Statistics in the downward category than in the non-down or very down categories.

Social Media Engagement of Stakeholders: A Decision Tree Approach in Container Shipping

Social media provides a significant avenue for stakeholder engagement which is crucial to ensure loyalty and satisfaction of stakeholders who possess valuable resources that can influence the business outcomes. Container lines – imperative members of global supply chains and facilitators of international trade – utilize social media to engage their stakeholders due to environmental and commercial complexity of their business. However, not all social media posts generate the same amount of stakeholder engagement. This study aims to identify and examine the social media post characteristics that lead to higher stakeholder engagement in the container shipping market. The study applies Chi-Squared Automatic Interaction Detection method to categorize social media posts based on their engagement levels. The analysis is conducted on the tweets of four global container lines which are posted between 1 September 2018 and 31 January 2019. The results demonstrate that social media posts of container lines have varying effects on engagement level. We found that fluency of tweets, tangibility of company resources in the tweet, vividness level, content type, existence of a link, and existence of a call-to-action significantly influence the container lines’ stakeholder engagement rate. This study is the first that finds out social media post classes based on the interaction between their characteristics and engagement rates by employing a decision tree methodology. The results are expected to help container lines in their social media management and stakeholder engagement policies.

The Investigation of the Relationship Between Mathematics and Science Literacy and Information and Communication Technology Variables

This study attempted to identify the information and communication technology items that affected students’ mathematics and science literacy scores by making use of the 2015 PISA data, The presence of numerous items related to ICT in the PISA and the administration of these items to large groups of people provides researchers with a large data source. However, researchers experience challenges in revealing the significant and beneficial data among the entire data set. So one of the most commonly used data mining method is the Chi-squared Automatic Interaction Detection method (CHAID), which is the decision tree method. As a result of the CHAID analysis, conducted to reveal the ICT items related to mathematics literacy scores, it was revealed that there was a significant relationship between mathematics literacy scores and the eight variables. For science literacy, there was a ten significant relationship variables. There is a relationship between high science and mathematics literacy scores and using digital devices at an early age as well as feeling comfortable with using digital devices at home. As an outcome of the CHAID algorithm, the realization of a significant reduction was achieved in the dimensionality of both models. The selected variables can be used for future research and development of new, parametric models. In the resulting model, apart from the reduction of the number of predictors, the reduction of their categories was also achieved.

Digital Recruitment and Acceptance of a Stepwise Model to Prevent Chronic Disease in the Danish Primary Care Sector: Cross-Sectional Study.

BackgroundDuring recent years, stepwise approaches to health checks have been advanced as an alternative to general health checks. In 2013, we set up the Early Detection and Prevention project (Tidlig Opsporing og Forebyggelse, TOF) to develop a stepwise approach aimed at patients at high or moderate risk of a chronic disease. A novel feature was the use of a personal digital mailbox for recruiting participants. A personal digital mailbox is a secure digital mailbox provided by the Danish public authorities. Apart from being both safe and secure, it is a low-cost, quick, and easy way to reach Danish residents.ObjectiveIn this study we analyze the association between the rates of acceptance of 2 digital invitations sent to a personal digital mailbox and the sociodemographic determinants, medical treatment, and health care usage in a stepwise primary care model for the prevention of chronic diseases.MethodsWe conducted a cross-sectional analysis of the rates of acceptance of 2 digital invitations sent to randomly selected residents born between 1957 and 1986 and residing in 2 Danish municipalities. The outcome was acceptance of the 2 digital invitations. Statistical associations were determined by Poisson regression. Data-driven chi-square automatic interaction detection method was used to generate a decision tree analysis, predicting acceptance of the digital invitations.ResultsA total of 8814 patients received an invitation in their digital mailbox from 47 general practitioners. A total of 40.22% (3545/8814) accepted the first digital invitation, and 30.19 % (2661/8814) accepted both digital invitations. The rates of acceptance of both digital invitations were higher among women, older patients, patients of higher socioeconomic status, and patients not diagnosed with or being treated for diabetes mellitus, chronic obstructive pulmonary disease, or cardiovascular disease.ConclusionsTo our knowledge, this is the first study to report on the rates of acceptance of digital invitations to participate in a stepwise model for prevention of chronic diseases. More studies of digital invitations are needed to determine if the acceptance rates seen in this study should be expected from future studies as well. Similarly, more research is needed to determine whether a multimodal recruitment approach, including digital invitations to personal digital mailboxes will reach hard-to-reach subpopulations more effectively than digital invitations only.

Classification of the length of study based on the student characteristics and academic performance in FMIPA Unsyiah

The ideal length of study for undergraduate students is about four to five years, but the fact is there are still many students of FMIPA Unsyiah takeslonger time to graduate. This research aims to classify the factors that affect that lenght of study based on the provided data in FMIPA Unsyiah, which are some data of characteristics and educational background of the students. The statistical methods used are CHAID (Chi-Squared Automatic Interaction Detection). CHAID method will divide the data into groups of smaller based linkages between response variable with the explanatory variables.This research used the data from undergraduate students who have graduated from the period of January 2007 to August 2014. It is found that the significant factors influencing the students’ lenght of study are the GPA, the education level of supervisor, the final exam score, the gender, and the program of study.value of the session. It is also found that the students whose lenght of study in time and average are students with excellent GPA score, received grade A in their final exam, and are female. While students within late lenght of study category are students in satisfactory GPA with the level education of their final writing supervisors are doctorate. In terms of classification accuracy, the percentage of accuracy ofthe model is 61.4%.

Implementation of Chi Square Automatic Interaction Detection (CHAID) Method to Identify Type 2 DiabetesMellitus in Tuberculosis Patient. A Case Study in Cipto Mangunkusumo Hospital

A Pulmonary Tuberculosis (pulmonary TB) is a chronic infectious disease caused by Mycobacterium tuberculosis. Chronic infection cause the body in a state of oxidative stress. In the state of stress, stress hormone production increases and can affect the increase of blood sugar levels which then trigger the occurrence of diabetes mellitus (DM). The purpose of this study is to determine the factors associated with the emergence of DM Type 2 and make a classification to characterize DM Type 2 in patients with pulmonary TB. In this research, the data used are secondary data of pulmonary TB patients obtained from Cipto Mangunkusumo Hospital (RSCM) for six years, 2012 - 2017. Chi Square Automatic Interaction Detection (CHAID) method is used to classify categorical data by dividing the data set into subgroups. The dependent variable is type 2 DM status, and the independent variables are gender, age, body mass index, level of neutrophil, level of lymphocytes, erythrocyte sedimentation rate, and anti-tuberculosis medicines such as rifampicin, isoniazid, pyrazinamide, ethambutol, and streptomicin. There are seven classes are obtained by classification using CHAID. The result from analysis of CHAID method shows that the factors related to the occurrence of type 2 DM on TB patients are age, body mass index, gender and pirazinamid (Z). Based on CHAID method, a classification of the occurrence of type 2 DM on pulmonary TB patients is obtained, which is pulmonary TB patients who are ≥ 40 years old, have body mass index ≥ 25 kg/m2, and the most patients are male (Class-4th).