Decision Tree Regression Analysis Research Articles

Decision Tree Regression Analysis of Proposed Metric Suite for Software Fault Prediction

Decision Tree Regression Analysis of Proposed Metric Suite for Software Fault Prediction

Cytokine Levels in Saliva Are Associated with Salivary Gland Fibrosis and Hyposalivation in Mice after Fractionated Radiotherapy of the Head and Neck.

Cytokines are mediators of inflammation that could lead to fibrosis. The aim was to monitor cytokine levels in saliva and serum after locally fractionated radiotherapy of the head and neck in mice and investigate associations with salivary gland fibrosis and hyposalivation. C57BL/6 mice were randomized to sham or X-ray irradiation of 66 Gy in 10 fractions over 5 days. Blood and saliva were collected on days -7, 5, 35, 80, and 105 following cytokine analysis. The harvested submandibular salivary gland was assessed for the presence of fibrosis. Decision tree regression analysis was used to investigate whether cytokine levels could predict late endpoints in terms of hyposalivation or fibrosis. Significant formation of fibrosis in gland tissue and reduced saliva production was found after irradiation. The pro-inflammatory cytokines IL-1α, TNF, TIMP1, G-CSF, KC, and MIP-1α showed increased levels in saliva in irradiated mice and a strong correlation with late endpoints. The decision tree analysis largely separated controls from irradiated animals, with IL-1α being the strongest predictor. Pro-inflammatory cytokines in saliva, but not in serum, were associated with late endpoints. This indicates that cytokine expression in saliva is a good biomarker for local salivary gland damage with IL-1α as the strongest single predictor.

The core-periphery image of South Korea on the Chinese tourist market in the times of conflict over THAAD.

Abstract This study investigates how bilateral political tensions influence tourists' perceptions of a strong national brand, as well as their attitudes toward and intent to visit the country, using network and decision-tree regression analyses. The context of the study is the conflict between South Korea and its primary tourist market, China, over the deployment of the Terminal High Altitude Area Defense (THAAD) system (July 2016–December 2017). From April to June of 2018, the study collected the most salient images of Chinese tourists associated with South Korea and organized them in the core-periphery network of South Korea's image. It was found that while the overall image was positive, the core cluster contained conflict-specific negative images. With respect to attitude and visit intention, images that differentiated among tourists belonged to the peripheral clusters, and not to the core cluster. Practical implications of the study findings are suggested.

Use of high frequency oscillatory ventilator in neonates with respiratory failure: the clinical practice in Taiwan and early multimodal outcome prediction

High-frequency oscillatory ventilation (HFOV) can be a rescue for neonates with refractory respiratory failure or an early elective therapy for preterm infants with severe respiratory distress syndrome (RDS). However, little is known about the current evolution and therapeutic limitations of HFOV. We therefore aimed to describe its use in clinical practice and predict the risk of mortality for neonates receiving HFOV. A retrospective observational study of all neonates treated with HFOV in a quaternary referral NICU between January 2007 and December 2016 was conducted. We classified these patients into five subgroups based on primary respiratory diagnoses. We performed the logistic regression and decision tree regression analyses to identify independent factors of 30-day mortality following HFOV. A total of 1125 patients who were ever supported on HFOV were enrolled, of whom 64.1% received HFOV as a rescue therapy, 27.2% received it as an elective therapy, and 8.7% received it for air leak. An average oxygenation index (OI) greater than 25 in the first 24 hours after the initiation of HFOV and patients with secondary pulmonary hypertension were found to have the greatest risk of in-hospital mortality (p < 0.0001). The overall in-hospital mortality rate was 25.8% (290/1125). Decision tree regression analysis revealed that neonates with refractory respiratory failure who had a pre-HFOV OI value higher than 20.5 and OI values higher than 21.5, 23.5 and 34 at 2 hours, 6 hours, and 12 hours after the use of HFOV, respectively, had a significantly increased risk of 30-day mortality. We identified the predictors and cutoff points of OI before and after the initiation of HFOV in neonates with respiratory failure, which can be clinically used as a reference for 30-day mortality. Further efforts are still needed to optimize the outcomes.

Differences among participants’ response to a wellness intervention for individuals with spinal cord injuries

BackgroundIndividuals with spinal cord injuries (SCI) often struggle with lifestyle adjustment following injury. Effective wellness interventions may aid in improving quality of life; however, treatment response heterogeneity is a concern for behavioral interventions. ObjectiveTo identify differences among adults with SCI who were responders and non-responders in a wellness intervention. MethodsThere were 29 persons with SCI who received a wellness intervention. Participants engaged in six in-person workshops over a 3-month period, led by wellness experts with peer mentors available. Individual intervention participant change was compared to the mean among control group participants target outcomes variables (i.e., satisfaction with life, self-efficacy for health practices, secondary conditions, health promoting behaviors, and perceived stress) to classify responders versus non-responders. ResultsOf the 29 participants, 18 who completed the intervention were classified as responders and 11 were non-responders. The mean age was 43.2 ± 11.2 years, and years post-injury were 12.0 ± 9.8. Decision tree regression analysis for demographic variables and injury characteristics showed that single/divorced participants were more likely to not respond to the intervention (52%), compared to married participants (17%). Further, participants who were single/divorced and ≤50 years old were more likely to not respond to the intervention (62%) than those who were older (25%). ConclusionsSingle/divorced individuals with SCI may respond differently to behavioral wellness interventions than married individuals. Additionally, the effects of age on treatment response in behavioral interventions should be further examined. Identifying non-modifiable factors related to response heterogeneity may help guide the creation of tailored interventions specific to patient characteristics.

Using Decision Tree to Predict Response Rates of Consumer Satisfaction, Attitude, and Loyalty Surveys

Response rate has long been a major concern in survey research commonly used in many fields such as marketing, psychology, sociology, and public policy. Based on 244 published survey studies on consumer satisfaction, loyalty, and trust, this study aimed to identify factors that were predictors of response rates. Results showed that response rates were associated with the mode of data collection (face-to-face &gt; mail/telephone &gt; online), type of survey sponsors (government agencies &gt; universities/research institutions &gt; commercial entities), confidentiality (confidential &gt; non-confidential), direct invitation (yes &gt; no), and cultural orientation (individualism &gt; collectivism). A decision tree regression analysis (using classification and regression Tree (C&amp;RT) algorithm on 80% of the studies as the training set and 20% as the test set) revealed that a model with all above-mentioned factors attained a linear correlation coefficient (0.578) between the predicted values and actual values, which was higher than the corresponding coefficient of the traditional linear regression model (0.423). A decision tree analysis (using C5.0 algorithm on 80% of the studies as the training set and 20% as the test set) revealed that a model with all above-mentioned factors attained an overall accuracy of 78.26% in predicting whether a survey had a high (&gt;50%) or low (&lt;50%) response rate. Direct invitation was the most important factor in all three models and had a consistent trend in predicting response rate.

Estimating California Bearing Ratio Using Decision Tree Regression Analysis Using Soil Index and Compaction Parameters

California Bearing Ratio is used as an index of soil strength and bearing capacity. In the machine learning theory, a decision tree algorithm can help us to define preferences, risks, benefits and targets. In this study, decision tree algorithm was employed for estimating California Bearing Ratio from the soil index and compaction parameters. There were seven inputs and one output in the study. In the analysis, we employed gravel, sand, fine grain, liquid limit, plastic limit, maximum dry unit weight and optimum water as inputs and California Bearing Ratio as output. The number of data was 124. In the decision tree algorithm, data were divided two for train and test groups. And, 10-fold cross validation process was applied to data in the analysis. Consequently, fine grain values used as input in the study were carried out to be very determinative for regression analysis. Decision tree regression analysis estimation indicated strong correlation (R = 0.89) between the output and target. It has been shown that the correlation equations obtained as a result of regression analysis are in satisfactory agreement with the test results.

Machine learning reveals orbital interaction in materials

We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments of the constituent atoms in bimetal alloys of lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordination numbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regression analyses using the OFM.