Classification Tree Method Research Articles

Estudio del rendimiento académico mediante la comparación de modelos de regresión y árboles de clasificación

This article aims to identify the factors that affect academic performance by comparing regression models and decision trees to determine the factors involved. The methodology adopted is quantitative in nature, focused on the collection of numerical data and its statistical analysis, in order to evaluate the relationships between different variables and determine those factors that influence academic performance. The population studied includes remedial students in the statistics career, who underwent an exploratory and descriptive analysis, using two statistical methods. Two modeling techniques were used: multinomial logistic regression and classification trees. The variables evaluated included sociodemographic factors, previous academic performance, and characteristics of the educational environment. The results showed that the logistic regression model achieved 100% accuracy with an AUC of 1, indicating perfect classification ability. In comparison, the classification tree model had an accuracy of 70.83% with an AUC of 0.7042, reflecting moderate classification ability. From these results, key factors that affect academic performance were identified, such as study habits, interest in the career and psychological aspects. In conclusion, multinomial logistic regression was more effective and accurate in analyzing the quantitative relationships between the variables that affect academic performance, outperforming the classification tree method.

Tracking Seasonal Coastal Dynamics of Akyatan Wetland with Landsat 8 image using Object-Based Classification and Regression Tree

In recent years, the management of water and wetlands has become increasingly important. Akyatan Wetland is a natural lake formed by the Ceyhan River, located to the west of the river. The area around the wetland is home to various sea and land creatures, as well as endemic plants and endangered birds. This study aimed to analyze the seasonal changes occurring in the Akyatan wetland in 2016. Landsat 8 images from four different seasons of 2016 were utilized to identify these changes. The study areas were classified into six categories: lake, sea, cultivated agricultural land, soil, building area, and water channel. Thematic maps for the four seasons were generated using the object-based Classification and Regression Tree (CART) method. Additionally, change detection analyses were conducted using the post-classification comparison method. The study revealed a decrease in the lake area from winter to spring, followed by an increase from spring to summer and summer to autumn in 2016. This fluctuation was attributed to groundwater, river water, and melting snow.

Current and Potential Land Use/Land Cover (LULC) Scenarios in Dry Lands Using a CA-Markov Simulation Model and the Classification and Regression Tree (CART) Method: A Cloud-Based Google Earth Engine (GEE) Approach

Current and Potential Land Use/Land Cover (LULC) Scenarios in Dry Lands Using a CA-Markov Simulation Model and the Classification and Regression Tree (CART) Method: A Cloud-Based Google Earth Engine (GEE) Approach

Predictors for recurrence of chronic osteomyelitis following long bones cavity defect replacement with synthetic materials

Background. Recent scientific literature only has a few studies highlighting the factors affecting the development of recurrent infection after surgical treatment of chronic osteomyelitis. Aim of the study: to determine significant risk factors of infection recurrence during one- and two-stage treatment of a cavity defect with synthetic materials in patients with chronic osteomyelitis and to create an assessment scale for prediction of the treatment outcome based on this data. Material and Methods. The retrospective study comprised 131 patients with chronic osteomyelitis of the long bones. The patients were divided into two groups based on the treatment outcome: group 1 (n=90) - successful elimination of infection; group 2 (n=41) - recurrent osteomyelitis after one-stage or during two-stage treatment. Differences between the groups were assessed to identify factors that affect the risk of recurrence. Multivariate analysis was performed using the classification tree method ​​to develop a scale for the comprehensive risk assessment of osteomyelitis recurrence (CRAOR). Results. The following factors affecting the treatment outcome were assessed: location (r=0.205, p=0.019); duration of osteomyelitis (r=0.23, p=0.007); history of debridement operations (r=0.264, p=0.002); bone defect volume (r=0.175, p=0.045); physiological class according to Cierny-Mader (r=0.188, p=0.004); anatomical type of osteomyelitis according to Cierny-Mader (r=0.15, p=0.086); nature of the pathogen (r=0.123, p=0.162). The degree of the osteomyelitis recurrence risk, threshold values ​​for quantitative factors and the total score the scale were established. In patients with a score 5, the risk of osteomyelitis recurrence increased by 8.1 times in one-stage treatment compared to patients with a score ≤5 (OR 8.143, CI 1.008-65.882). In two-stage treatment, patients with a score 5 had the risk of osteomyelitis recurrence increased by 4.1 times compared to patients with a score ≤5 (OR 4.059, CI 0.461-35.714). Based on the scale, an algorithm of treatment for these patients was developed.

Economic and environmental factors determining spatial variation in soil liming in Poland

The aim of this research was to identify the key factors determining the level of soil liming in Poland. Spatial analyses were conducted using data from the 2020 National Agricultural Census. The study presents regional variations in selected parameters that characterise agricultural production quality and structural features, including the market connections of farms. The Classification and Regression Trees (C&RT) method was employed to identify the factors that most significantly influence the intensity of liming in the examined districts. These factors include the share of permanent grasslands, labour inputs, the percentage of farms engaged in commercial production, farm size, and the level of mineral fertilisation (NPK). The results indicate that regions with larger farms and higher NPK fertilisation tend to apply more lime. In contrast, areas with a higher share of permanent grasslands and labour-intensive activities show lower levels of liming. These findings suggest that improving agricultural practices, particularly in regions with small farms and limited fertilisation, could enhance soil liming efforts and align them with the European Green Deal's goals for sustainable agriculture.

Investigation of Higher Education Students' Interest in Sports and Usage Levels of University Sports Facilities

Introduction The size, number, capacity, characteristics and quality of the areas where sports activities can be carried out in universities are among the leading indicators of student investment. Objective: To determine the interest and attitudes of university students in the Western Black Sea region towards sports, their level of utilisation of university sports facilities, and whether university sports facilities significantly predict students' interest and attitudes towards sports. Methodology: The classification and regression tree method (CART), one of the data mining methods, was used through a relational screening model. Data were collected from 1462 associate and undergraduate students (789 males and 673 females) studying in different departments of 7 universities in the Western Black Sea Region in 2022-2023 with two scales of Ceyhun (2015, 2017). Results: It was determined that the areas where university students do sports, their level of interest in sports, their age, class level, duration of doing sports and their active participation in sports affect their level of utilisation of sports facilities. Discussions: It was observed that the level of interest in sports of female university students was higher than that of males, and the level of interest in sports of students who do sports was negatively correlated with the duration of doing sports. Conclusion: The efficient use of sports facilities, one of the main factors of sports participation in universities, is directly proportional to the university's facilities and individual factors.

Modeling of Naïve Bayes and Decision Tree Algorithms to Analyze Sentiment Related to Jaklingko Public Transportation on Social Media X (Twitter)

Technological developments have changed the way people access information and share opinions through the internet and social media such as X (Twitter). Public sentiment is now crucial in evaluating public services, especially in the era of advanced information technology. As a metropolitan city, public transportation in DKI Jakarta plays an important role in economic, business and government activities. The Jaklingko initiative launched by the DKI Jakarta Provincial Government aims to provide efficient and affordable public transportation. This research implements and compares Naive Bayes and Decision Tree classification methods to perform sentiment analysis of twitter users opinions regarding Jaklingko into positive and negative categories. Data was collected by crawling using tweet_harvest which obtained 6001 tweets about Jaklingko, then text preprocessing was carried out. Word weighting is done using the TF-IDF method to give value to each term in the document, and sentiment labeling is done using the Vader Lexicon library. The data is divided into training and testing data with a ratio of 80%:20% for the classification process. Evaluation of the method is done using confusion matrix. The results showed that the accuracy of Naive Bayes reached 84.9% and Decision Tree reached 84.2%. The wordcloud visualization depicts negative words including vehicle stoppage, bad driver attitude, and envy from people in other cities. Meanwhile, positive words included free system, useful programs, and user convenience. This research provides an in-depth understanding of public opinion towards Jaklingko, with potential implications for improving public transportation services in Jakarta.

The Spatiotemporal Evolution of the Mudflat Wetland in the Yellow Sea Using Landsat Time Series

Mudflat wetland, one of the 27 surface elements identified by the International Geographic Data Committee, has undergone substantial transformations with the rapid growth of the social economy and marine hazards, resulting in significant changes in its area and distribution. Quick identification of mudflat wetland evolution is vital to improve the mudflat ecological service value. We employed object-oriented and decision tree classification methods to map the mudflat wetland in the Yellow Sea using the Landsat time series from 1983 to 2020. The Improved Spectral Water Index (IWI) was established by combining the characteristics of many ratio indices and using ratio operation and quadratic power operation. The coefficient of variation (CV) of the IWI was calculated, and the range of the intertidal zone in 1983, 1990, 2000, 2010, and 2020 was obtained by using a threshold method. The results indicate that the mudflat wetland area decreased continuously from 1983 to 2020, with a reduction of 337.38 km2/10a. Among the total area, the natural wetland experienced a decline of 446.9 km2/10a, with the most drastic changes occurring between 2000 and 2010. In contrast, the area of the human-made wetland increased by 109.56 km2/10a. Over the 38 years, the tidal flat has undergone the most drastic reduction, with an average of 157.45 km2/10a. From 1983 to 2020, the intertidal zone area decreased, with a reduction of 429.02 km2/10a. Human activities were the key factors causing mudflat wetland loss. Based on these findings, we propose several policy suggestions. This study provides a scientific basis for understanding the synergetic evolution mechanism of coastal resources utilization and mudflat wetland protection under global change.

Using mathematical models in the assessment of the circulatory system in employees working under harmful occupational conditions

Introduction. According to the results of medical examinations of metallurgists, in the structure of newly identified somatic morbidity, circulatory system diseases (CSD) are one of the leading pathologies. To assess the prevalence of CSD in workers in harmful working conditions, it is possible to use various mathematical solutions as a tool. The objective of the study was to substantiate the possibility of using variance analysis and classification tree (CT) methods to assess the prevalence of circulatory diseases in industrial workers. Materials and methods. Data of medical examinations and a questionnaire-based survey of three hundred eight workers of steel production were input into a database. The interrelationship between CSD and individual and occupational risk factors was examined using a one- and two-way ANOVA. The classification tree method was used for a multivariate analysis. Results. The one- and two-way ANOVA revealed a statistically significant correlation between the CSD family history (p=0.001) and age, years of occupational exposure, overweight, blood glucose level, total cholesterol (p<0.05) and the CSD prevalence in the steel workers. We applied the classification tree method to make decisions on referring the subjects to potentially “healthy” and “sick” workers by determining the CSD prevalence in relation to various combinations of risk factors. Limitations. This study is limited to the results of medical examinations of the workers collected over a one-year period. Conclusions. Our experience in using the analysis of variance and classification tree to evaluate the CSD prevalence in ferrous industry employees allows recommending them for use in assessing the prevalence of various diseases in workers exposed to occupational hazards.

Classification and regression tree (CART) for predicting cadmium (Cd) uptake by rice (Oryza sativa L.) and its application to derive soil Cd threshold based on field data

The entry of Cd into soil-rice systems is a growing concern as it can pose potential risks to public health. To derive regional soil Cd threshold, a total of 333 paired soil and rice samples was collected in Anhui Province, Eastern China. The results showed that the total soil Cd and soil Zn/Cd were the most significant variables contributing to Cd content in polished rice. The Chinese Soil Quality Standards might overestimate risk posed by Cd-contaminated soil for rice production in the mining area due to high Zn/Cd values of some mining-associated soils. Cd levels in polished rice can be predictable using stepwise multiple linear regression (MLR) model. However, the derived soil Cd threshold based on the MLR model would be unrealistically high. The classification and regression tree method (CART) performed well in simulating Cd levels in polished rice and can be used to derive soil Cd threshold instead of MLR to minimize the uncertainty.

Evaluating missing data handling methods for developing building energy benchmarking models

This study explored methods for handling missing data in the development of machine learning-based energy benchmarking models, assessing their training time, performance, and variance. Unlike the common assumption of missing completely at random, this study adopted a missing at random (MAR) perspective, which is more appropriate for building data. We compared the inherent missing data handling method of extreme gradient boosting (XGBoost) with the Median, k-nearest neighbors (KNN), and classification and regression trees (CART) methods, alongside Shapley additive explanation (SHAP) method for model interpretability. The findings indicate that, despite its computational demands, the CART method most accurately mirrors the original data distribution, thereby enhancing model performance and stability. The KNN method is effective, while the XGBoost method is viable under computational time constraints. This work highlights the importance of reliable test data for performing accurate evaluations of imputation methods. These results offer guidelines for the selection of imputation methods in model development, contributing to the improved accuracy of energy benchmarking models. The MAR-based approach for missing data analysis holds promise for future research on building energy data, providing crucial insights for accurate energy benchmark model performance assessments.

PUBLIC STIGMA ABOUT POLYGAMY BASED ON ISLAMIC-MUHAMMADIYAH VIEWS USING SENTIMENT ANALYSIS APPROACH

Social media is very important to control the development of issues that occur today. With social shifts and changing societal values, polygamy has become a complex issue and attracts the attention of many people around the world discussed through social media platforms. This research contributes to the field by applying a sentiment analysis approach to automatically detect and analyze public sentiment regarding polygamiy content on Twitter, particularly in the context of Islamic-Muhammadiyah views. This study used decision tree classification methods, support vector machines, and random forests with the best analysis accuracy obtained at SVM 77.4%. Furthermore, the results of the sentiment class obtained were analyzed according to the views of Muhammadiyah. The results obtained in the analysis 77% commented negatively and 23% commented positively. In addition, this research can be used as a reference for future research on sentiment analysis cases to training and testing classroom models.

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

The predictive role of lung clearance index on FEV1 decline in cystic fibrosis.

The lung clearance index (LCI) is a sensitive lung function index that is used to detect early lung disease changes in children with cystic fibrosis (CF). This study aimed to define the predictive role of baseline LCI, along with other potential factors on the change in forced expiratory volume in one second (FEV1) during one-year follow-up in CF patients who had a percent predicted (pp) FEV1≥80. LCI was concurrently performed on 57 CF patients who had ppFEV1 ≥80 at month zero. The ppFEV1 decline was evaluated prospectively during the one year follow up. The primary outcome of ppFEV1 decline in the study group in one year was dichotomized according to the median value for the decline in ppFEV1, which was 3.7. The LCI value predicting ppFEV1 decline at the end of one year was calculated with receiver operating characteristic curve analysis. Regression analysis was performed. Furthermore, a decision tree was constructed using classification and regression tree methods to better define the potential effect of confounders on the ppFEV1 decline. The LCI value for predicting ppFEV1 decline >3.7% at the end of one year was 8.2 (area under the curve: 0.80) Multivariable regression analysis showed that the absence of the F508del mutation in at least one allele, LCI >8.2 and initial FEV1 z-score were predictors of a ppFEV1 decline >3.7 (p<0.001). Factors altering ppFEV1 decline>3.7% at the end of one-year evaluated by decision trees were as follows: initial FEV1 z-score, type of CFTR mutation, LCI value and initial weight-for-age z-score. LCI is sensitive for predicting ppFEV1 decline in patients with ppFEV1 ≥80 along with the initial FEV1-z-score and type of CFTR mutation.

A dataset of the sugarcane planting distribution with the spatial resolution of 10 m in Guangxi from 2018 to 2020

The extraction of sugarcane planting distribution is the basis of crop monitoring. The extraction of timely and accurate sugarcane planting distribution is of great significance for monitoring sugarcane crops and making adjustments to the planting structure. Based on Sentinel-1 and Sentinel-2 data, in this paper, we adopted the active and passive remote sensing collaborative method and decision tree classification method to carry out research on the extraction method of sugarcane planting distribution in Guangxi. Then we verified the data by referring to multi-year survey ground sample data. On this basis, we produced dataset of the sugarcane planting distribution with the spatial resolution of 10 m in Guangxi from 2018 to 2020. The remote sensing extraction-based method yielded an overall accuracy of 92% for sugarcane planting distribution in Guangxi in 2018, with a Kappa coefficient of 0.8. The overall accuracy of sugarcane planting distribution in Guangxi in 2019 is 94%, with a Kappa coefficient 0.88; the overall accuracy of sugarcane planting distribution in Guangxi in 2020 is 94%, with a Kappa coefficient of 0.88. This dataset can be used as the basic data for the analysis of temporal and spatial changes of sugarcane in Guangxi. And it can also provide basic data support for the optimization and adjustment of sugarcane production management and planting structure in Guangxi.

Sociodemographic and psychological factors affecting motor vehicle crashes (MVCs): a classification analysis based on the contextual-mediated model of traffic-accident involvement

The study aimed to determine the sociodemographic and psychological profiles of drivers with a history of motor vehicle crashes (MVCs), following the contextual-mediated model of crash involvement, and trying to define similarities and differences with drivers without MVCs. Although road trauma prevention has become a central public health issue, the study of psychological determinants of MVCs does not have consistent results due to methodological and theoretical weaknesses. Three-hundred and forty-five active drivers (20% females) completed an extensive office-based fitness-to-drive evaluation including measures of cognition, personality, self-reported driving-related behaviors, attitudes, as well as computerized measures of driving performance. The Classification and Regression Tree method (CART) was used to identify discriminant predictors. The classification identified several relevant predictors; the personality trait of Discostraint (as a distal context variable; cut-point: 50 T points) and motor speed (as a proximal context variable; cut-point: 64 percentile ranks). The global classification model increased approximately 3 times the probability of identifying people with a history of MVC involvement, starting from an estimated prevalence of being involved in an MVC in a period of five years in the population of active drivers. Consistent with the ‘contextual-mediated model of traffic accident involvement’, the results of the present study suggest that road trauma analysis should focus on both distal and proximal driver-related factors by paying attention to their association in determining MVCs. These results represent a valuable source of knowledge for researchers and practitioners for preventing road trauma.

Classification of Land Suitability For Soybean Crops Using The Cart Method and Feature Selection Using an Algorithm ABC

The allocated area for soybean cultivation has been gradually decreasing, leading to a decline in both production and productivity. Consequently, the current level of soybean production and productivity falls short of meeting the demand within the community. One potential solution to augment soybean output and efficiency involves allocating specific parcels of land for soybean cultivation. It is essential to conduct land evaluations tailored to soybean cultivation, accounting for the land's inherent potential, in order to optimize land utilization. Thus, a comprehensive system is required to assess land suitability, particularly for soybean cultivation, and employ the results of this classification as recommendations for land allocation. This research employess combination the Classification and Regression Tree (CART) method and the Artificial Bee Colony (ABC) algorithm to classify suitable land for soybean cultivation. CART is used for classification and ABC is utilized for feature selection to identify the most relevant attributes in case of the algorithm improvement. Through a series of iterative experiments involving 5, 10, 25, 50, 75, and 100 iterations, the best attribute was determined following three attempts at each iteration. The Confusion Matrix test yielded an accuracy rate of 94.22% for the CART method in the second experiment, while the combined use of the best ABC and CART combination resulted in an accuracy rate of 97.11%. Therefore, it can be concluded that the integration of the artificial bee colony (ABC) algorithm with the classification and regression tree (CART) method outperforms the sole use of the CART method in terms of accuracy.

Responding to challenge call for machine learning model development in diagnosing respiratory disease sounds

The normal and abnormal sounds from the respiratory system shed great light on medical science by revealing the quality, diseases and changes in people’s lungs. In medicine, this easy and old method, realized by the stethoscope, facilitates the diagnosis of diseases by specialists. This manual method can sometimes lead to wrong decisions in terms of sound detection due to different audibility. Detailed sound analysis is crucial for accurately detecting lung diseases with high mortality rates. As technology advances, the development of automated approaches based on machine learning is of great interest as they provide modern and highly accurate analysis. In today’s most popular topic, i.e., the COVID-19 disaster, the conflict between early detection of respiratory disease and machine learning for sound signal processing is extremely important. In this study, a machine learning model was developed to detect respiratory system sounds such as sneezing/coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine learning model was developed in this study, which was a strong response to the challenge called the “Pfizer digital medicine challenge” on the “OSFHOME” open access platform. In the database provided in this challenge, which consists of 3 parts, features that effectively showed coughing/sneezing sound analysis were extracted from training, testing and validating samples. Based on the Mel frequency cepstral coefficients (MFCC) feature extraction method, mathematical and statistical features were prepared. The sequential forward selection (SFS) feature selection method was used to select the relevant and dominant variables among the obtained features to represent the model fully and accurately. Three different classification techniques were considered for successful respiratory sound classification in the dataset containing more than 3800 sounds. Support vector machine (SVM) with radial basis function (RBF) kernels, decision tree and ensemble aggregation classification methods were used as classification techniques. In an attempt to classify coughing/sneezing sounds from other sounds, SVM with RBF kernels was achieved with 83% success.

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI.

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

Using Interactive Decision Tree Models in Artillery Fuse Testing

In the introduction, the concept of interactive trees is defined and the purpose of the study is presented. Then, the RGM-2 fuse is described, as are the results of its tests which served as a basis for building specific models. The types of ammunition in which this variation of an artillery fuse is used are listed. A method of building interactive classification trees, allowing the author of the model to interfere with its structure, is described as well. Models of interactive classification trees, such as C&amp;RT, CHAID and XAID have been designed and built. For each model, a tree diagram, a predictor importance sheet, a risk assessment sheet, and a summary of the observed and predicted values are presented. The method of interacting with the constructed classification tree structures, whose task was to improve the designed models, is shown using the examples of two models. The analysis of the models built after the interaction has been performed and, based on the obtained results, the best designed model was selected.