Decision Tree Classification Model Research Articles

Identifying popular, rejected and neglected children in Chinese preschool: An exploratory study on the educational application of spatial positioning data

Amidst the rapid advancement of Internet of Things (IoT) technology and the burgeoning field of Multimodal Learning Analytics (MMLA), this study employs spatial positioning technology as a case study to investigate the potential of multimodal data in assessing children's social development. This study combines the spatial positioning data of preschool children collected during free play sessions in natural educational settings and the spatial metrics constructed based on observational studies to establish and validate a sociometric status Decision Tree classification model. The findings suggest that the model can overall accurately identify children with three distinct sociometric statuses, albeit with some variability in efficacy across different sociometric groups and age groups. Notably, the model demonstrates a high hitting rate in identifying the potentially neglected children, providing valuable support for educators in understanding and fostering children's developmental needs. This study also highlights the advantages of emerging technology and multimodal data application in child development assessment.

Developing a prototype system of computer-aided appointment scheduling: A radiology department case study.

Scheduling patient appointments in hospitals is complicated due to various types of patient examinations, different departments and physicians accessed, and different body parts affected. This study focuses on the radiology scheduling problem, which involves multiple radiological technologists in multiple examination rooms, and then proposes a prototype system of computer-aided appointment scheduling based on information such as the examining radiological technologists, examination departments, the patient's body parts being examined, the patient's gender, and the patient's age. The system incorporated a stepwise multiple regression analysis (SMRA) model to predict the number of examination images and then used the K-Means clustering with a decision tree classification model to classify the patient's examination time within an appropriate time interval. The constructed prototype creates a feasible patient appointment schedule by classifying patient examination times into different categories for different patients according to the four types of body parts, eight hospital departments, and 10 radiological technologists. The proposed patient appointment scheduling system can schedule appointment times for different types of patients according to the type of visit, thereby addressing the challenges associated with diversity and uncertainty in radiological examination services. It can also improve the quality of medical treatment.

Rule-enriched Decision Tree Classifier for Conditional Sentence Sentiment Analysis

Conditional sentences are often used when people have to choose with some requirements. Conditional sentences account for more than 8% of user opinions. Although accounting for a considerable amount, for sentiment analysis methods, the sentiment expressed in conditional sentences is still analyzed as typical narrative sentences. This causes the approaches to fail to achieve maximum performance. To solve this problem, although some studies have proposed separate approaches to sentiment extraction and analysis for conditional sentences, there are very few, and the performance still needs to improve. This study proposes a new classifier based on a decision tree classifier model enriched with rules (called Rule-enriched Decision Tree Classifier (ReDTC)) to extract and analyze sentiments expressed in conditional sentences. ReDTC has been experimented on a dataset collected from English teaching websites. The performance gain demonstrates that the proposed ReDTC method significantly improved the performance in sentiment extraction and analysis in conditional sentences.

LOAN STATUS PREDICTION USING DECISION TREE CLASSIFIER

This paper investigates the effectiveness of the Decision Tree Classifier in predicting loan status, a critical task in the financial sector. The study utilizes a dataset containing various attributes of loan applicants such as income, credit score, employment status, and loan amount. The dataset is preprocessed to handle missing values and categorical variables. Feature importance is analyzed to understand the key factors influencing loan approval decisions. A Decision Tree Classifier model is trained and evaluated using performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate the feasibility of using Decision Trees for loan status prediction and provide insights into the decision-making process of loan approval.

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

After a mine water inrush occurs, it is crucial to quickly identify the source of the water inrush and the key control area, and to formulate accurately efficient water control measures. According to the differences in water chemical characteristics of four aquifers in the Fenyuan coal mine, the concentrations of K+~Na+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− were taken as water source identification indexes. A decision tree classification model based on the C4.5 algorithm was adopted to visualize the chemical characteristics of a single water source and extract rules, and intuitively obtained the discrimination conditions of a single water source with Mg2+, Ca2+, and Cl− as important variables in the decision tree: Mg2+ < 39.585 mg/L, Cl− < 516.338 mg/L and Mg2+ ≥ 39.585 mg/L, Ca2+ < 160.860 mg/L. Factor analysis and Fisher discriminant theory were used to eliminate the redundant ion variables, and the discriminant function equations of the two, three, and four types of mixed water sources were obtained successively in turn. This paper puts forward MSE, RMSE, and MAE as the evaluation indexes of the water source mixing degree calculation models and obtains the ranking of the pros and cons of the mixed water source mixing degree calculation models. The results show that the minimum inscribed circle analytical method is the optimal model for the calculation of the mixing degree of two types of water sources, and the MSE, RMSE, and MAE are 0.17%, 4.13%, and 4.13%, respectively. The minimum inscribed circle clustering method is the optimal model for the calculation of the mixing degree of three types of water sources, and the minimum distance method is the optimal model for the calculation of the mixing degree of four types of water sources. The method of mine water source identification based on the decision tree C4.5 algorithm and mixing degree calculation has the characteristics of a simple calculation process, high efficiency, objective accuracy, and low cost, which can provide a scientific basis for the development of stope water control measures.

Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning.

Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two. Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI. Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered. There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.

Autism Spectrum Disorder Prediction in Children Using Machine Learning

Life symptoms associated with autism spectrum disorder (ASD) typically manifest during childhood and persist into adolescence and adulthood. ASD, which can be caused by genetic or environmental factors, can be significantly improved through early detection and treatment. Currently, standardized clinical tests are the primary diagnostic method for ASD. However, these tests are time consuming and expensive. Early detection and intervention are pivotal in enhancing the long-term prospects of children diagnosed with ASD. Machine-learning (ML) techniques are being utilized alongside conventional methods to improve the accuracy and efficiency of ASD diagnosis. Therefore, the paper aims to explore the feasibility of employing support vector machines, random forest classifier, naïve Bayes, logistic regression (LR), K-nearest neighbor, and decision tree classification models on our dataset to construct predictive models for predicting and analyzing ASD problems across different age groups: children, adolescents, and adults. The proposed techniques are assessed using publicly available nonclinical ASD datasets of three distinct datasets. The four ASD datasets, namely toddlers, adolescents, children, and adults, were obtained from publicly available repositories, specifically Kaggle and UCI ML. These repositories provide a valuable data source for research and analysis related to ASD. Our main objective is to identify the susceptibility to ASD in children during the early stages, thereby streamlining the diagnosis process. Based on our findings, LR demonstrated the highest accuracy for the selected dataset.

Evaluating Climate and Community Health Care Infection Risks through Computational Predesign

This study highlights the value of computational modeling in health care infection prevention planning, especially in response to climate-induced community risk factors. It investigates the relationship between Socioecological Modeling features and Principal Component Analysis derived parameters in a Decision Tree classification model, predicting regional risk levels of Clostridioides difficile Infection (CDI) and Methicillin-resistant Staphylococcus aureus health care-associated infections (MRSA) Hospital Acquired Infections (HAI). The research confirms that climate-related factors, such as housing instability and medical infrastructure strain, impact regional infectivity risks, influencing health care environment resilience. It emphasizes the link between Clostridioides difficile and MRSA HAI, highlighting the role of Built Environment Vulnerability. These findings have vital implications for hospital predesign, material selection, layout, and hand sanitization device choices, and underscore optimizing patient flow in health care environment planning.

Factor analysis and prediction of startups and ways to exit based on decision tree classification models with adaptive k with SMOTE method for imbalance problem

This paper focuses on factor analysis to combine the information of startups with an synthetic minority over-sampling technique (SMOTE) method via an aspect of the decision tree algorithms that assist investors in project screening for describing important features.However, the investment of a startup company has characteristics of imbalanced data. Improvements in the handling of imbalanced data based on the SMOTE method has been developed by sampling from the minority class. The problem is how to set optimized k-nearest neighbors among the most common feature values. This work purposed a method to fit data in the startup’s information that is designed to handle the data value by adaptive k with SMOTE, which manages the problem with an imbalance class label for robustness of evaluation metrics for balancing the portion of multi-class. The adaptive k experimental results can solve the k parameter setting and produce a high accuracy rate of startup companies’ class as closed, operating, and acquired status of investment at 0.84, 0.87 and 0.97 respectively. The overall accuracy rate is 0.99; that is the best outcome compared with other methods for handling imbalance. In addition, the results and discussion shown that can meet the needs of investment startup are designed and discussed of business views and machine learning views to work co-operation.

Retracted: Construction of College Chinese Blended Teaching Mode Based on Decision Tree Classification Model in New Media Context.

[This retracts the article DOI: 10.1155/2022/4608631.].

Distributed Denial-of-Service (DDoS) Attack Detection using 1D Convolution Neural Network (CNN) and Decision Tree Model

The major problem of internet security is a Distributed Denial-of-Service (DDoS) attack, which can’t be detected easily. This attack is said to have occurred when lots of service requests are simultaneously received at a server on the internet. This makes the server too busy to provide normal services for others. The Distributed Denial of Service (DDoS) attacks nature on large networks on the Internet demanding to develop the effective detection and response methods. The deployment of these technique should perform not only at the network core but also at the edge. A DDoS attack detection framework is presented based on transfer learning model consisting of 1D Convolution Neural Network (CNN) and decision tree classifier. The 1D CNN model utilizes for features extraction from the input network traffic data. This operation also reduces the dimension of the data thereby removing the redundancy in the data. These features are given to the decision tree model for classification. The proposed framework identified the DDoS attacks with good accuracy. This system could identify attacks in real-time and provide network security.

Factors influencing the use of information and communication technologies by students for educational purposes

Implementation of information and communication technologies (ICTs) in education is defined as the incorporation of ICTs into teaching and learning activities, both inside and outside the classroom. Despite widely studied, there is still no consensus on how it affects student performance. However, before evaluating this, it is crucial to identify which factors impact students' use of ICT for educational purposes. This understanding can help educational institutions to effectively implement ICT, potentially improving student results. Thus, adapting the conceptual framework proposed by Biagi and Loi (2013) and using the 2018 database of the Program for International Student Assessment (PISA) and a decision tree classification model developed based on CRISP-DM framework, we aim to determine which socio-demographic factors influence students' use of ICT for educational purposes. First, we categorized students according to their use of ICT for educational purposes in two situations: during lessons and outside lessons. Then, we developed a decision tree model to distinguish these categories and find patterns in each group. The model was able to accurately distinguish different levels of ICT adoption and demonstrate that ICT use for entertainment and ICT access at school and at home are among the most influential variables to predict ICT use for educational purposes. Moreover, the model showed that variables related to teaching best practices of Internet utilization at school are not significant predictors of such use. Some results were found to be country-specific, leading to the recommendation that each country adapts the measures to improve ICT use according to its context.

Fake News Detection Using Machine Learning

Today one of the fattest environments for opinion exchanging is the internet. Individuals can share their data or post any news they want through social media platforms. these data sharing platforms do not verify the users or the data and information they post. so, some users can share fake or untrusted data through these platforms on the Internet. Fake news is described as a propaganda tool against any Individual, society, government, or political party. Humans cannot detect and understand all the fake news or data through these online platforms. In this study, the challenge has been defined through a Machine learning concept. machine learning classification was applied to the dataset. Finally, a comparison of the working of these classifiers is presented along with the results, Decision Tree and Support Vector Machine classification models are performing well.

Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework’s network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%.

Deep Mining Algorithm of Online Purchase Behavior Data Based on Decision Tree Model

Abstract It is difficult to mine online buying behavior data by ignoring the classification of online buying behavior data, and the precision and recall are both on the low side. The training set of online buying behavior data is processed by top-down recursion, and a single decision tree is created recursively, and a decision tree classification model is constructed. Based on the classification results of behavior data, the regular estimation of online shopping features is calculated by preprocessing customer behavior features, and the deep mining algorithm is designed. Experimental results show that the decision tree model has good data clustering effect. Based on this, the precision and recall of online shopping behavior data mining are high, and the application performance is ideal.

Machine-Learning-Based Classification for Pipeline Corrosion with Monte Carlo Probabilistic Analysis

Pipeline corrosion is one of the leading causes of failures in the transmission of gas and hazardous liquids in the oil and gas industry. In-line inspection is a non-destructive inspection for detecting corrosion defects in pipelines. Defects are measured in terms of their width, length and depth. Consecutive in-line inspection data are used to determine the pipeline’s corrosion growth rate and its remnant life, which set the operational and maintenance activities of the pipeline. The traditional approach of manually processing in-line inspection data has various weaknesses, including being time consuming due to huge data volume and complexity, prone to error, subject to biased judgement by experts and challenging for matching of in-line inspection datasets. This paper aimed to contribute to the adoption of machine learning approaches in classifying pipeline defects as per Pipeline Operator Forum requirements and matching in-line inspection data for determining the corrosion growth rate and remnant life of pipelines. Machine learning techniques, namely, decision tree, random forest, support vector machines and logistic regression, were applied in the classification of pipeline defects using Phyton programming. The performance of each technique in terms of the accuracy of results was compared. The results showed that the decision tree classifier model was the most accurate (99.9%) compared with the other classifiers.

Factors Influencing Prevalence and Intensity of Haemosporidian Infection in American Kestrels in the Nonbreeding Season on the Baja California Peninsula, Mexico

ABSTRACT Overwintering areas can act as reservoirs for haemosporidians, and their blood-sucking vectors may easily survive and flourish in warm climates. The American Kestrel (Falco sparverius) is a small-sized raptor that includes both resident and migratory individuals that overwinter in agriculture areas of the Baja California peninsula, Mexico. We measured and collected blood samples from 54 individual kestrels captured in a fragmented habitat located within a matrix of agricultural and native scrub in the southern part of the peninsula during the autumn and winter of 2018–2019 and 2019–2020. We identified and quantified haemosporidians in blood smears of each individual. We used decision tree classification models of prevalence and intensities of haemosporidians to assess the importance of season, host body size, and environmental characteristics of the capture site (proximity to the nearest infected host, number of ponds used for irrigation, Normalized Difference Vegetation Index [NDVI], distance to the edge of the agricultural matrix, and distance to the nearest city). Overall prevalence was 42.6%, with an average intensity of 2.4 haemosporidian-infected cells per 2000 erythrocytes. Most haemosporidians (91.3%) were identified as Haemoproteus tinnunculi, and the rest as Plasmodium spp. The prevalence of haemosporidians increased with pond availability, the distance from the edge of the agricultural matrix, and the proximity of infected individuals. Parasite intensity increased with the availability of ponds, the distance to the edge of the agriculture matrix, and the NDVI. Spatial and temporal patterns of parasite prevalence and intensity illustrated local infection dynamics, suggesting a complex scenario of haemosporidian transmission in migratory and resident kestrels.

Glass Classification and Identification based on Decision Tree and Random Forest Classification Models

Glass artifacts are weathered due to age and improper preservation, etc. This paper analyzes the classification laws of high potassium glass and lead-barium glass based on the data; for each category, suitable chemical compositions are selected to classify them into subcategories, and firstly, a decision tree classification model is used to analyze the classification laws of the two categories of glass by substituting the data set for machine learning, and specific classification methods and classification results are given. And the chemical composition of the unknown category of glass artifacts is analyzed, and the random forest classification model is used to predict the training set and test set by.

Biochemical identification of prepubertal boys with Klinefelter syndrome by combined reproductive hormone profiling using machine learning.

Klinefelter syndrome (KS) is the most common sex chromosome disorder and genetic cause of infertility in males. A highly variable phenotype contributes to the fact that a large proportion of cases are never diagnosed. Typical hallmarks in adults include small testes and azoospermia which may prompt biochemical evaluation that typically shows extremely high FSH and low/undetectable inhibin B serum concentrations. However, in prepubertal KS individual biochemical parameters are largely overlapping those of prepubertal controls. We aimed to characterize clinical profiles of prepubertal boys with KS in relation to controls, and to develop a novel biochemical classification model to identify KS before puberty. Retrospective, longitudinal data from 15 prepubertal boys with KS and data from 1475 controls were used to calculate age- and sex-adjusted standard deviation scores (SDS) for height and serum concentrations of reproductive hormones and used to infer a decision tree classification model for KS. Individual reproductive hormones were low but within reference ranges and did not discriminate KS from controls. Clinical and biochemical profiles including age- and sex-adjusted SDS from multiple reference curves provided input data to train a 'random forest' machine learning (ML) model for detection of KS. Applied to unseen data, the ML model achieved a classification accuracy of 78% (95% CI, 61% - 94%). Supervised ML applied to clinically relevant variables enabled computational classification of control and KS profiles. The application of age- and sex-adjusted SDS provided robust predictions irrespective of age. Specialized ML models applied to combined reproductive hormone concentrations may be useful diagnostic tools to improve the identification of prepubertal boys with KS.

Zero Crossing Point Detection in a Distorted Sinusoidal Signal Using Decision Tree Classifier

Zero-crossing point detection in a sinusoidal signal is essential in the case of various power systems and power electronics applications like power system protection and power converters controller design. In this paper, 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, a decision tree classifier is used to predict the zero crossing point in a distorted signal based on input features like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classifier model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classifier is able to predict the zero-crossing points in a distorted signal with maximum accuracy of 98.3 % for noise signals and 100 % for harmonic distorted signals.