Chi-square Automatic Interaction Detection Decision Research Articles

The role of mobile fitness applications in student leisure activities

The purpose of this study is to examine the behavior of students when using mobile applications (apps) during physical activity and to identify the determinants of their behavior. Analysis of variance, t test, chi-square test of independence, and chi-squared automatic interaction detection decision trees were utilized. Exploratory analysis was undertaken to identify the motivation behind the use of apps, using the self-determination theory as a framework. The results showed that the main reason for using apps is to record and save data for personal use and to improve the effectiveness of training. Students mostly use apps while running and cycling. The determinants of student app use are gender, place of residence, material situation, and level of higher education (bachelor’s or master’s degree). The results of the exploratory analysis indicate that motivations for using apps for most surveyed students are autonomous. The results provide a greater understanding of the role of mobile app use during leisure.

Predictors of Quadriceps Strength Asymmetry after Anterior Cruciate Ligament Reconstruction: A Chi-Squared Automatic Interaction Detection Decision Tree Analysis.

The influence of graft type on the restoration of quadriceps strength symmetry after ACL reconstruction (ACLR) has been widely studied. However, an important consideration when evaluating quadriceps symmetry is the fact that this measure can be influenced by numerous factors beyond graft type. This study sought to determine if graft type is predictive of quadriceps strength asymmetry during the first 12 months post-ACLR taking into consideration potentially influential factors (i.e., age, sex, body mass index, time post-ACLR). We retrospectively reviewed quadriceps strength data from 434 patients (303 female patients and 131 male patients) who had previously undergone ACLR with an autograft (hamstring tendon, quadriceps tendon [QT], patellar tendon [PT]) or allograft. Chi-Squared Automatic Interaction Detection decision tree analysis was used to evaluate if graft type is predictive of quadriceps strength asymmetry during the first 12 months post-ACLR taking into consideration age, sex, body mass index, and time post-ACLR. The best predictor of quadriceps strength asymmetry was graft type. Specifically, three graft categories were identified: 1) allograft and hamstring tendon autograft, 2) PT autograft, and 3) QT autograft. The average quadriceps strength asymmetry for each of the three identified categories was 0.91, 0.87, and 0.81, respectively, and differed statistically from each other ( P < 0.001). The second-best predictor of quadriceps strength asymmetry was sex, albeit only in the PT and QT groups (with female patients having increased asymmetry). Female patients post-ACLR with a QT autograft were at highest risk for quadriceps strength asymmetry. Graft type and sex are important predictors of quadriceps strength asymmetry after ACLR. Clinicians should take these factors into consideration when designing rehabilitation protocols to restore quadriceps strength symmetry during the postoperative period.

Subjective assessment underestimates surgical risk: On the potential benefits of cardiopulmonary exercise testing for open thoracoabdominal repair.

BackgroundInitial clinical evaluation (ICE) is traditionally considered a useful screening tool to identify frail patients during the preoperative assessment. However, emerging evidence supports the more objective assessment of cardiorespiratory fitness (CRF) via cardiopulmonary exercise testing (CPET) to improve surgical risk stratification. Herein, we compared both subjective and objective assessment approaches to highlight the interpretive idiosyncrasies.MethodsAs part of routine preoperative patient contact, patients scheduled for major surgery were prospectively “eyeballed” (ICE) by two experienced clinicians before more detailed history taking that also included the American Society of Anesthesiologists score classification. Each patient was subjectively judged to be either “frail” or “not frail” by ICE and “fit” or “unfit” from a thorough review of the medical notes. Subjective data were compared against the more objective validated assessment of postoperative outcomes using established CPET “cut‐off” metrics incorporating peak pulmonary oxygen uptake, V̇O2PEAK at the anaerobic threshold (V̇O2‐AT), and ventilatory equivalent for carbon dioxide that collectively informed risk stratification. These data were retrospectively extracted from a single‐center prospective National Health Service database. Data were analyzed using the Chi‐square automatic interaction detection decision tree method.ResultsA total of 127 patients were examined that comprised 58% male and 42% female patients aged 69 ± 10 years with a body mass index of 29 ± 7 kg/m2. Patients were poorly conditioned with a V̇O2PEAK almost 20% lower than predicted for age, sex‐matched healthy controls with 35% exhibiting a V̇O2‐AT < 11 ml/kg/min. Disagreement existed between the subjective assessments of risk with ∼34% of patients classified as not frail on ICE were considered unfit by notes review (p < .0001). Furthermore, ∼35% of patients considered not frail on ICE and ∼31% of patients considered fit by notes review exhibited a V̇O2‐AT < 11 ml/kg/min, and of these, ∼28% and ∼19% were classified as intermediate to high risk.ConclusionsThese findings highlight the interpretive limitations associated with the subjective assessment of patient frailty with surgical risk classification underestimated in up to a third of patients compared to the validated assessment of CRF. They reinforce the benefits of a more objective and integrated approach offered by CPET that may help us to improve perioperative risk assessment and better direct critical care provision in patients scheduled for “high‐stakes” surgery including open thoracoabdominal aortic aneurysm repair.

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.

Highway infrastructure and safety implications of AV technology in the motor carrier industry

This study explores the infrastructure investments needed to support the adoption of autonomous vehicles and provides potential safety benefits from AV technologies as a possible motivation for such investments. We assume that the motor carrier industry will be (one of) the first adopter(s) of such technologies, and therefore is the focus of this study. Using large truck crash data from 2013 through 2015 obtained from the Missouri State Highway Patrol, Chi-square Automatic Interaction Detection decision trees are estimated to examine the effect of AV technologies on motor carrier crash severity. Results suggest that the greatest contributory predictors of crash severity outcomes are driving too fast for conditions, distracted/inattentive driving, overcorrecting and driving under the influence of alcohol. If these circumstances are altered by AV technologies, it is suggested that between 117 and 193 severe crashes involving large trucks could be prevented annually in Missouri alone. To render such safety benefits, key vehicle needs include autonomously controlling acceleration and steering, monitoring of the environment, and responding to dynamic driving environments without the need for human intervention. Importantly, the safe operations of a system that can perform such AV tasks require readable lane markings to capitalize on potential safety benefits.

Verification of two machine learning approaches for cloud masking based on reflectance of channel IR3.9 using Meteosat Second Generation over Middle East maritime

ABSTRACT Most cold channels of Meteosat Second Generation (MSG) satellites can distinguish between the sea and ice cloud tops, except for the IR3.9 channel because of the close reflectance and radiance values of the IR3.9 channel for maritime, low-level cloud and ice cloud tops. In this article, we introduce and evaluate two machine learning methods for cloud masking of Spinning Enhanced Visible and Infrared Imager (SEVIRI) images in the day and night that use the reflectance value of the IR3.9 channel. We reached a good correlation by comparing the results of the modelled cloud masking of Meteosat satellite images with MODIS (Moderate Resolution Imaging Spectroradiometer) and CLM (Cloud Mask product of EUMETSAT) images in a way that the coefficient of determination (R 2) value was 92.34%, 89.91% and 83.69%, 78.23% in the cold season and 90.17%, 87.09% and 80.37%, 76.48% in the warm season, respectively, using the CHAID (chi-squared automatic interaction detection) decision tree and RBF (radial basis function) neural network approaches.

Developing a Process for Deploying Systemic Countermeasures at Unsignalized Intersections: Case Study of Virginia

This paper discusses the process used to develop a safety improvement plan for unsignalized intersections using systemic low-cost countermeasures. The scope of this project focused on unsignalized intersections with stop sign control on the minor approaches. The first objective was to perform an assessment of Virginia’s unsignalized intersection crashes over a five-year period to determine predominant crash trends and collision types to target for treatment. The four focus collision types with the highest frequency of crashes and the greatest potential reduction in crashes were 3-leg angle, 3-leg fixed object off the road, 4-leg angle and 4-leg rear end. Chi-square automatic interaction detection decision tree analysis was used to perform a systemic analysis to identify a group of intersections associated with potential risk factors related to the focus collision types. A tiered list of systemic countermeasures to deploy was developed. The countermeasures were intended to warn of the stop ahead, make the stop sign and stop location more visible on a minor street, and to warn of the intersection ahead on a major street. The potential for safety improvement measure was used to prioritize the candidate treatment intersections. Before deployment, a study of the intersection by district traffic engineering staff was planned to finalize the plan. The output from the research was a safety improvement plan to systemically deploy treatments to unsignalized intersections as part of the safety program.

Using principal component analysis to develop performance indicators in professional rugby league

ABSTRACTPrevious research on performance indicators in rugby league has suggested that dimension reduction techniques should be utilised when analysing sporting data sets with a large number of variables. Forty-five rugby league team performance indicators, from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, were reduced to 10 orthogonal principal components with standardised team scores produced for each component. Forced-entry logistic (match outcome) and linear (point’s difference) regression models were used alongside exhaustive chi-square automatic interaction detection decision trees to determine how well each principle component predicted success. The 10 principal components explained 81.8% of the variance in point’s difference and classified match outcome correctly ~90% of the time. Results suggested that if a team increased “amount of possession” and “making quick ground” component scores, they were more likely to win (β = 15.6, OR = 10.1 and β = 7.8, OR = 13.3) respectively. Decision trees revealed that “making quick ground” was an important predictor of match outcome followed by “quick play” and “amount of possession”. The use of PCA provided a useful guide on how teams can increase their chances of success by improving performances on a collection of variables, instead of analysing variables in isolation.

Team performance indicators that predict match outcome and points difference in professional rugby league

Performance indicators allow for the objective quantification of performance, however, limited PI research for professional rugby league exists. Therefore, this paper assessed 24 relative PIs (home value minus away) from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, amounting to 567 matches. Backwards logistic (match outcome) and linear (points difference) regression models were used alongside exhaustive Chi-Square Automatic Interaction Detection decision trees to identify performance indicators (PIs) and key performance indicators. Teams had a higher chance of winning and would gain more points when they scored first (OR = 1.6, β = 2.4) and increased completed sets (OR = 1.2, β = 1.2) by one unit. Conversely, teams had a lower chance of winning when they increased scoots (OR = 0.9, β = −0.2). However, some PIs which were thought to be important (as identified by previous literature) were removed from the analysis thus calling into question the appropriateness of stepwise methods. Future research may consider utilising dimension reduction techniques when analysing large data-sets that encompass multiple variables.

Ski injury predictive analytics from massive ski lift transportation data

Ski injury research is traditionally studied on small-scale observational studies where risk factors from univariate and multivariate statistical models are extracted. In this article, a large-scale ski injury observational study was conducted by analyzing skier transportation data from six consecutive seasons. Logistic regression and chi-square automatic interaction detection decision tree models for ski injury predictions are proposed. While logistic regression assumes a linearly weighted dependency between the predictors and the response variable, chi-square automatic interaction detection assumes a non-linear and hierarchical dependency. Logistic regression also assumes a monotonic relationship between each predictor variable and the response variable, while chi-square automatic interaction detection does not require such an assumption. In this research, the chi-square automatic interaction detection decision tree model achieved a higher odds ratio and area under the receiver operating characteristic curve in predicting ski injury. Both logistic regression and chi-square automatic interaction detection identified the daily time spent in the ski lift transportation system as the most important feature for ski injury prediction which provides solid evidence that ski injuries are early-failure events. Skiers who are at the highest risk of injury also exhibit higher lift switching behavior while performing faster runs and preferring ski slopes with higher vertical descents. The lowest injury risk is observed for skiers who spend more time in the ski lift transportation system and ski faster than the average population.

Patterns of attendance to health checks in a municipality setting: the Danish 'Check Your Health Preventive Program'.

We aimed to investigate the determinants of attendance to a preventive health check program and to explore the homogeneity of the attenders.4853 eligible persons living in the municipality of Randers, Denmark, from 2012 to 2013, aged 30–49 years, received an invitation to attend the ‘Check Your Health Preventive Program’. Data was obtained from the Danish National Registers. Socio-demographic factors, use of preventive services, morbidity were examined as determinants of attendance by Poisson regression analyses. A chi-squared automatic interaction detection decision tree analysis was used to identify mutually exclusive groups.In total, 55% of the invited population attended (49% men). Attenders were more likely to be: of higher age; immigrants; cohabiting; have: higher socio-economic status; higher use of preventive services and lower morbidity. Decision tree analysis revealed six groups, with the most important variable being income: 1) low income, low education (A = attendance rate: 38%; P = population size: 11%); 2) low income, education higher than 10 years, living alone (A: 41%; P: 5%); 3) low income, education higher than 10 years, cohabiting (A: 56%; P: 16%); 4) middle income (A: 60%; P: 34%); 5) high income, living alone (A: 56%; P: 4%); 6) high income, cohabiting (A: 69%; P: 30%).More than half of a general population voluntarily attended a general health check, despite a resource intensive offer. People with low resources had lower attendance rates. This study adds a detailed description of mutually exclusive groups of attenders, for use in future planning and implementation of preventive actions.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

Background:In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing.Methods:As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014.Results:The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve.Conclusions:The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

Using Decision Trees to Identify Tourism Stakeholders: The Case of Two Eastern North Carolina Counties

This paper explores stakeholder involvement in tourism planning, development, and management. For tourism planners to include stakeholders in the tourism planning process those stakeholders and their interests need to be identified. The research reported in this paper describes and applies an analytical technique that is not traditionally used to identify stakeholders. A questionnaire was developed and mailed to stakeholders in two rural communities in North Carolina. The data were analysed with an Exhaustive Chi-square Automatic Interaction Detection decision tree. From the results of the decision tree, stakeholder groups were identified in relation to their support for sustainable tourism development in their community.