Decision Tree Technique Research Articles

FUZZY-PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLER WITH INTERACTIVE DECISION TREE

The STATCOM is extensively used in the power system to address the power quality issues by actively compensating the reactive power requirements of the load. However, the STATCOM performance depends on the underlying controller operation to handle sudden load changes and disturbances optimally with faster response. To solve this issue, this paper presents the intelligent algorithm-optimized fuzzy rule-based Proportional Integral Derivative (PID) controller to enhance the performance of the STATCOM. The proposed controller consists of a fuzzy-PID controller capable of handling non-linear dynamics and sudden changes in the load. The interactive decision tree (IDT) technique detects weaker metrics and improves their influence in control scenarios. The Biogeography-based optimization (BBO) algorithm minimizes the controller's integral absolute error to tune the fuzzy-PID controller parameters. The algorithm terminates once all the metrics are tuned to satisfaction with the operator’s consent. The proposed IDT-based fuzzy PID controller is tested on a power system containing a nonlinear load, and its performance is compared with the existing controller and simulated using the MATLAB/Simulink tool. The proposed controller provides fast control action, reduces the reactive power from the source by 42.5%, and improves the power factor by 1.5%.

Recognizing human activities using light-weight and effective machine learning methodologies

Background Human activity recognition (HAR) is increasingly important in enhancing healthcare systems by enabling accurate monitoring of individuals' movements through sensor data. This paper is motivated by the need to improve the accuracy of HAR, particularly for applications in e-health systems, where reliable activity detection can lead to better health outcomes. The study explores six prominent machine learning techniques—decision tree, random forest, linear regression, Naïve Bayes, k-nearest neighbor, and neural networks—to determine which methods can most effectively predict activities like walking, sitting, standing, laying, walking upstairs, and walking downstairs. Methods We employed these six machine learning algorithms to analyze a comprehensive dataset derived from various sensors. Each model was rigorously trained and evaluated to compare its effectiveness in recognizing human activities. The experiments aimed to identify strengths and weaknesses in each approach, with particular emphasis on advanced techniques such as random forest, convolutional neural networks (CNNs), and gated recurrent networks (GRNs). Results The experimental evaluation revealed that the random forest classifier, CNN, GRN, and neural networks delivered promising results, achieving high accuracy levels. Notably, the neural network model excelled, attaining an impressive accuracy of 98%. In contrast, the Naïve Bayes model did not meet the performance expectations set by the other algorithms. Conclusions This research effectively classifies activities such as sitting, standing, laying, walking, walking downstairs, and walking upstairs, underscoring the potential of machine learning in HAR. The findings highlight the superior performance of neural networks in enhancing activity recognition, which could lead to advanced applications in e-health systems and improve overall healthcare monitoring strategies.

의사결정나무 분석에 대한 국내 연구 동향 탐색

This study explores the basic system and applicability of decision tree techniques by examining domestic research trends in this field. We analyzed 1,075 academic papers using domestic decision tree analysis from 2001 to 2023, categorized by year, journal, topic field, author, paper frequency, and frequency of algorithms used. Key findings reveal that the frequency of papers began with one in 2001, increasing to 87 in 2023. The distribution across fields was as follows: social science (377 papers, 35.07%), natural science (233 papers, 21.67%), and engineering (230 papers, 21.40%). Among the algorithms, CHAID was used most frequently (315 times, 36.42%), followed by CART (310 times, 35.84%), ensemble techniques like random forest (124 times, 14.34%), and C5.0 (94 times, 10.87%). The Quest algorithm was rarely used (10 times, 1.16%). The annual usage trends indicate a growing preference for ensemble techniques to enhance decision tree prediction rates. The paper's keywords included “decision trees” (687), “data mining” (239), “machine learning” (105), “logistic” (98), and “neural networks” (68). Notably, keywords related to health, such as “suicide,” “depression,” “high blood pressure,” and “health,” emerged prominently, signifying the extensive use of decision trees in medical research. Based on these findings, we propose implications and directions for follow-up studies.

AirNet: predictive machine learning model for air quality forecasting using web interface

Air is one of the most significant elements of the environment. The increasing global air pollution crisis poses an unavoidable threat to human health, environmental sustainability, ecosystems, and the earth's climate. Air pollution has been referred to as a silent killer due to its insidious nature. Its indirect impact on human health further underscores its dangerous effects. Early detection of air quality can potentially save millions of lives globally. A unique and transformative approach can harness the power of machine learning to combat air pollution. This research presents a manual and web-based automatic prediction system that provides real-time alerts on air quality status and can help prevent premature deaths, chronic diseases, and other health problems. Air pollutants, including carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM 2.5), are used in this study for feature analysis and extraction. The system utilizes publicly available data from 23,463 different cities worldwide. Data preprocessing was performed before feeding the data into the machine learning models for feature correlation and evaluation. The proposed research uses various machine learning models to predict air quality, including Random Forest (100%), Logistic Regression (79%), Decision Tree (100%), Support Vector Machine (93%), Linear SVC (98%), K-Nearest Neighbor (99%), and Multinomial Naïve Bayes (52%). A user-friendly Django-based web interface offers an accessible platform for users to monitor air quality in real-time, based on the two best-performing models: Random Forest and Decision Tree techniques.

Using Decision Tree Technique to Predict the Financial Collapse of Iraqi Private Banks: An Applied Study

Predicting the Financial Collapse (FC) using modern technologies and scientific methods for Iraqi private banks is considered an early warning to detect their failure in the future. Therefore, these proposals, represented by scientific methods and modern technologies, are submitted to the specialized bodies represented by (the Central Bank of Iraq) in order to present them to the banks in the research sample to raise their capacity through the use of modern technologies and in order to adopt them as a scientific basis for predicting the FC. The following will be followed: Evaluating the performance of banks and classifying them based on the Camels standard (CS) and predicting the FC based on modern technologies (by relying on the decision tree algorithm). The research relied on the deductive approach to complete the theoretical aspect and the applied (experimental) approach to complete the practical aspect. The researcher also processed and prepared the data and then evaluated the performance of banks and classified them based on the CS equations consisting of five indicators and on the averages of these results as inputs for prediction using DT. Different computer programs were also adopted, as the Excel program was used to unload the data, the (SPSS) program to apply the equations, and the (Matlab) program to predict modern technologies. The most prominent conclusions reached by the research are as follows: Modern technologies represented by decision trees are characterized by providing more accurate information

Cancer detection with various classification models: A comprehensive feature analysis using HMM to extract a nucleotide pattern

This work presents a novel feature extraction method for identifying complex patterns in genomic sequences by employing the Hidden Markov Model (HMM). In this study, we use HMM to identify gene nucleotide patterns that are specific to malignant and non-malignant cells. Crucial genetic components DNA and RNA are involved in many biological processes that impact both healthy and malignant cells. Early patient identification is essential to successful cancer diagnosis and therapy. Varying nucleotide patterns indicate different cellular responses, which are important to understanding the molecular causes of cancer and associated disorders. We present a detailed study of nucleotide patterns in whole raw nucleotide sequences with variations in both protein sequence (CDS) and non-protein sequence (NCDS) in both malignant and non-malignant cells. Nucleotide prediction has been achieved while computational expenses are reduced by using the proposed HMM for feature extraction and selection. The classification models implemented in this work for cancer detection are Gradient-Boosted Decision Trees (GBDT), Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM) with kernels. The suggested classification model's accuracy and 10-fold cross-validation have been validated via comprehensive case studies. The results reveal that DT and ensemble learning techniques significantly differentiate between malignant and non-malignant DNA sequences. SVM with suitable kernels improves cancer detection accuracy significantly. Combining feature reduction approaches with nucleotide pattern classifiers based on Hidden Markov models improves performance and ensures reliable cancer detection.

Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Differential Classification of Dengue, Zika, and Chikungunya Using Machine Learning—Random Forest and Decision Tree Techniques

Identifying Influential Variables on Health Expenditure of the Organisation for Economic Co-Operation and Development (OECD) Countries.

Health expenditures of countries have an increasing trend in general and identifying variables affecting health expenditure is an important step toward budget planning for financial sustainability. This study aimed to examine the health expenditure of the Organisation for Economic Co-operation and Development (OECD) countries and identify influential variables. The data for the years 2000-2018 of OECD countries' current health expenditure (% of GDP) and economic, demographic, and health variables, considered to affect the health expenditure, to include in the analysis were extracted using the World Bank database (World Bank 2021). Data analys using Chi-Squared Automatic Interaction Detection (CHAID) decision tree technique. Fifteen variables in economic, demographic, and health categories are selected to build the CHAID decision tree. As a result of CHAID analysis, five variables are identified as influential on current health expenditure, which are gross domestic product per capita, life expectancy at birth, death rate, out-of-pocket expenditure, and fertility rate. Thirty-seven OECD countries are classified into eleven groups by the decision rules in terms of the current health expenditure. The high value of the correlation coefficient between the predicted values and the actual values of health expenditure of countries indicates good prediction performance. Moreover, the regression models built using the identified influential variables as explanatory variables give good forecast accuracy. As an effective tool, the CHAID decision tree technique provides a rule-based model in the form of a tree with nodes and branches, illustrating the splitting process graphically with identified variables and their cut-off points for classification and prediction.

Water Contaminant Detection and Water Purification of Household Drinking Water Using a New Stacking Ensemble Model

ABSTRACTContaminated drinking water sources pose a significant health risk worldwide. Monitoring programs for drinking water quality aims to ensure safe water supply by informing management practices. Improved online monitoring of water systems is necessary as current lab‐based methods are slow and do not offer real‐time public health protection. Rapid detection and response to potential contamination events are crucial to mitigate health risks. Mark of‐purpose water treatment strategies offer a reasonable method for upgrading drinking water quality at the family level and forestalling waterborne illnesses. This study focuses on collecting household drinking water and utilizing various sensors to measure parameters such as pH, turbidity, water level, temperature, and humidity. A consistent water quality noticing system using the stacking outfit model, which solidifies Bayesian association and decision tree techniques, is proposed in this article. Bayesian network analyzes the input data attained from sensors collecting real‐time data and concludes whether the data represents the contamination event. DTs are utilized to demonstrate the connections between multivariate water boundaries utilized in the review. Afterward, a multiobjective, such as a biobjective optimization model and a nondominated genetic algorithm (NGA) are used in this work of optimization to minimize the volume of contaminated water. After the pollution in the water is identified, water decontamination processes are done given point of purpose medicines like ceramic channels and solar water disinfection (SODIS). The method outlined is executed through Python software. The findings indicate that the estimated values for PH, temperature, and turbidity are 7.3, 31.8, and 0.77, respectively. However, the proposed method is compared with the existing C‐NSGA‐II, while compared to this method, the proposed system produces improved cost functions. Consequently, suitable water treatment and supply should be considered to reduce the effects on people's health as well as to improve living conditions, respectively.

Quantitative assessment of population risk to tropical cyclones using hybrid modeling combining GAM and XGBoost: A case study of Hainan Province

Global climate change is expected to increase the proportion of intense tropical cyclones (TCs) in the Northwest Pacific. This study focused on how factors, especially extreme events, may affect disaster losses. To address this issue, an event-based multivariate TC risk assessment model, which employs copulas, a generalized additive model, and undersampling extreme gradient boosting decision tree techniques, is developed to enhance the accuracy of disaster loss prediction. The results suggest that on Hainan Island, the rate of the affected population is positively correlated with the maximum wind speed and maximum daily rainfall while negatively correlated with the gross domestic product and elevation. The study also shows that the TC risk in the cities of Hainan Island increases as the return period expands, and each return period scenario shows a unique geospatial distribution of TC risk on Hainan Island, with higher risks in the coastal and eastern regions. These results highlight the importance of implementing effective disaster management strategies to mitigate the impact of severe TCs in the region.

Enhancing agricultural wireless sensor network security through integrated machine learning approaches

AbstractWireless sensor network (WSN) works with a collection of multiple sensor nodes to fetch the data from the deployed environment to fulfill the application whether it is agricultural monitoring, industrial monitoring, etc. The agricultural region can be monitored by deploying sensor nodes to multiple verticals where continuous human presence is not feasible. These devices are equipped with limited resources and are easily vulnerable to various cyber‐attacks. The attacker can hack the sensor nodes to steal critical information from WSN devices. The cluster heads in the WSN play a vital role in the process of routing data packets and attackers launch malicious codes through sender nodes to hack or damage the cluster heads to shut down the entire deployed network of agricultural regions. This research paper proposes a framework to improve the security of WSNs by providing a shield to the cluster heads of the network using machine learning techniques. The experimental study of the paper includes the comparative analysis of three machine learning techniques decision tree classifier, Gaussian Naïve Bayes, and random forest classifier for predicting WSN attacks like flooding, gray hole, blackhole, and TDMA that are deployed to support the proposed WSN security framework on the attack dataset. The random forest classifier achieves an accuracy of 98%, Precision of 97.6%, Recall of 97.6%, and F1 score of 97.8% which is the maximum among the deployed machine learning techniques.

Predicting lower limb lymphedema after cervical cancer surgery using artificial neural network and decision tree models

PurposeThis study aimed to develop and validate accessible artificial neural network and decision tree models to predict the risk of lower limb lymphedema after cervical cancer surgery. MethodsWe selected 759 patients who underwent cervical cancer surgery at the Hunan Cancer Hospital from January 2010 to January 2020, collecting demographic, behavioral, clinicopathological, and disease-related data. The artificial neural network and decision tree techniques were used to construct prediction models for lower limb lymphedema after cervical cancer surgery. Then, the models’ predictive efficacies were evaluated to select the optimal model using several methods, such as the area under the receiver operating characteristic curve and accuracy, sensitivity, and specificity tests. ResultsIn the training set, the artificial neural network and decision tree model accuracies for predicting lower limb lymphedema after cervical cancer surgery were 99.80% and 88.14%, and the sensitivities 99.50% and 74.01%, respectively; the specificities were 100% and 95.20%, respectively. The area under the receiver operating characteristic curve was 1.00 for the artificial neural network and 0.92 for the decision tree model. In the test set, the artificial neural network and decision tree models’ accuracies were 86.70% and 82.02%, and the sensitivities 65.70% and 67.11%, respectively; the specificities were 96.00% and 89.47%, respectively. ConclusionBoth models had good predictive efficacy for lower limb lymphedema after cervical cancer surgery. However, the predictive performance and stability were superior in the artificial neural network model than in the decision tree model.

Healthcare Analysis Based on Diabetes Prediction Using a Cuckoo-Based Deep Convolutional Long-Term Memory Algorithm

In recent years, the demand for mobile medical applications utilizing the Internet of Things (IoT) for diabetes diagnosis has been progressively increasing. Diabetes is commonly known as a chronic illness that presents a significant danger to individuals, analysing when blood sugar levels surpass the typical range. If diabetes is not promptly treated, hyperosmolar conditions can lead to serious health issues like hyperglycaemia and possibly even death. Since early detection enables lifestyle changes that prevent the disease's progression, it is crucial for diabetes management and health systems. However, diabetes diagnosis has a long computational time and low prediction accuracy. To address this issue, we propose a Cuckoo-Based Deep Convolutional Long-Term Memory (CDC-LSTM) algorithm that increases accuracy by classifying diabetics or non-diabetics. Additionally, we utilize the Standardized Feature Scaler (SFS) method to normalize the variance data by removing the mean of each feature. Moreover, we select the optimal features in the diabetes dataset utilising the Filter-Based Decision Tree (FBDT) technique. Finally, the proposed CDC-LSTM method can be used to distinguish between diabetics and non-diabetics, improving the accuracy of identifying diabetic patients. Additionally, the proposed method can predict diabetes using performance assessments such as precision, recall, and F-measure. Furthermore, the method's accuracy can be improved to 95.18% compared to previous approaches.

Technical–tactical differences between female and male elite football: A data mining approach through neural network analysis, binary logistic regression, and decision tree techniques

The technical−tactical performance of women’s football has improved markedly in recent years. Despite this improvement, there are still differences between men’s football and women’s football. The objectives of this study were to know the technical and tactical key performance indicators (KPIs) that differentiate elite men’s and women’s football teams as well as to determine which statistical techniques demonstrate superior classification ability and interpretability in football terms. For this purpose, 768 matches corresponding to the latest editions of the UEFA Champions League, UEFA Euro and FIFA World Cup for men and women were analyzed. First, the differences at the bivariate level were analyzed using student’s t-test for independent sample ( p < 0.05) for the male and female teams. Secondly, three data mining classification algorithms were applied: (i) Artificial Neural Network (ANN), (ii) Binary Logistic Regression, and (iii) Decision Tree. Significant differences were found between men’s football and women’s football in variables related to technical elements such as lost balls (ES = 1.19), ball recoveries (ES = 1.00), and accurate passes (ES = 0.97), as well as regulatory aspects like fouls (ES = 0.59), successful tackles (ES = 0.46), and yellow cards (0.45). On the other hand, the classification models presented excellent or good predictive capability [Range AUC 0.774−0.982], with very small differences between the ANN’s and logistic regression models. This result justifies the use of simpler models as the linear regression model to understand the differences between men’s and women’s football. Moreover, the observed differences may offer insights for future efforts aimed at enhancing the performance of women’s football.

Customer Satisfaction Analysis Using the C4.5 Algorithm at Askara Minimarket

Customer satisfaction is one way to improve the quality of service in a company, service satisfaction is also a factor in the company's development. If the company does not have quality service to the company, the company will not be able to develop and compete with other companies. The relationship between the company and consumers must run well, before employees enter the shop area, employees must be trained first. After the consumer has finished shopping, the consumer must choose whether he is satisfied or dissatisfied in the application, the results of the consumer's satisfaction and dissatisfaction will be entered into the data, this data will later be processed into Data Mining. The C45 algorithm is a data classification algorithm using a decision tree technique that is well known and preferred because it has advantages

Enhancing IoT data confidentiality and energy efficiency through decision tree-based self-management

Given the widespread deployment of IoT devices characterized by heterogeneity and energy limits, a greater emphasis on IoT data security within IoT environments has become required. This critical situation encouraged the deployment of lightweight encryption solutions to avoid additional attacks. These solutions, which are based on traditional encryption algorithms, are intended to fulfill the demands of resource-constrained devices while maintaining a high security level. In this research, we provide a novel IoT framework that uses lightweight cryptographic ciphers and the Autonomic Computing paradigm to self-manage the security of IoT devices. Our framework is based on a decision tree technique to guarantee the IoT data confidentiality by selecting and changing in an autonomic manner the most appropriate lightweight encryption algorithm to be used in order to improve energy efficiency by extending the lifespan of IoT devices and to provide an adapted security level according to IoT application type and IoT object state.

CART Classification on Ordinal Scale Data with Unbalanced Proportions using Ensemble Bagging Approach

CART is one of the algorithms in data exploration techniques with decision tree techniques. Unbalanced class proportions in the classification process can cause classification results of minor data to be incorrect. One way to overcome the problem of data imbalance is to use an ensemble bagging algorithm. The bagging algorithm utilizes the resampling method to carry out classification so that it can reduce bias in imbalanced data. The data used is secondary data from Fernandes and Solimun's 2023 research report. The number of sample are 100 respondents that has been valid and reliable. The sample for this research was mothers with toddlers in Wajak village, Malang Regency. The results showed that the ensemble bagging CART method is better at overcoming the problem of imbalance in the proportion of classes with a performance value of accuracy, sensitivity, specificity, and F1-Score values of 85%, 94.1%, 66.7%, and 78%. This research is limited to the Sumberputih Village area. So, the results of this research are only representative for the Wajak District area.

Climate finance across sub-Saharan Africa: Decision trees and network flows

The structure of climate finance flows from donors (multilateral sources) to recipients (sub-Saharan African countries) was studied. This is the first study to provide a comprehensive network structure of the climate finance flows into Africa, based on the global public climate finance governance system. Network theory and decision tree techniques were employed. The results obtained generally fit the multilateral funding units (MFUs) into two categories: central funding units (CFUs), which simultaneously attend to the climate-related projects of many African countries, and the boundary funding units (BFUs), which cater to only a few countries at once. An isolated region with no BFUs was identified. African countries within this group could be more exposed to climate financial risk as they rely on only the CFUs. In general, with the exception of mitigation REDD (reducing emissions from deforestation and forest degradation) climate finance, a disproportionate distribution of climate themes, with particular reference to adaptation finance, was observed across sub-Saharan Africa. This has real implications for equitable resource allocation of climate funds. The need for African-bred region-wide MFUs is recommended.

Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine

PurposeThis study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students.MethodsWe employed K-means clustering analysis algorithm and Decision Tree (DT) technique to identify the association between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters for improving ML models’ accuracy. In addition, t-tests and Analysis of Variance (ANOVA) were performed to identify significant differences between the clusters. We evaluated the models on a balanced dataset of 755 female participants from the Hebron district in Palestine.ResultsOur study found that 34.8% of the participants were anemic. The intake of various micronutrients (i.e., folate, Vit A, B5, B6, B12, C, E, Ca, Fe, and Mg) was below RDA/AI values, which indicated an overall unbalanced malnutrition in the present cohort. Anemia was significantly associated with intakes of energy, protein, fat, Vit B1, B5, B6, C, Mg, Cu and Zn. On the other hand, intakes of protein, Vit B2, B5, B6, C, E, choline, folate, phosphorus, Mn and Zn were significantly lower in anemic than in non-anemic subjects. DT classification models for vitamins and minerals (accuracy rate: 82.1%) identified an inverse association between intakes of Vit B2, B3, B5, B6, B12, E, folate, Zn, Mg, Fe and Mn and prevalence of anemia.ConclusionsBesides the nutrients commonly known to be linked to anemia—like folate, Vit B6, C, B12, or Fe—the cluster analyses in the present cohort of young female university students have also found choline, Vit E, B2, Zn, Mg, Mn, and phosphorus as additional nutrients that might relate to the development of anemia. Further research is needed to elucidate if the intake of these nutrients might influence the risk of anemia.

Main Predictor(s) of Good Corporate Governance in National Government Departments: A Decision-tree Classification and Prediction Analyses

Purpose: Good public sector corporate governance leads to good management, stewardship of public wealth, public engagement, and ultimately, better outcomes for citizens. As South Africa has one of the worst Gini coefficients in the world, its public sector should effectively address the challenges of inequality, poverty, and unemployment. Yet, almost 30% of national and provincial government departments obtain unfavourable audit outcomes. Methodology: Supporting the drive for clean audit outcomes that equate to good corporate governance, the research objective was to determine the predictors of national government departments’ corporate governance success. The study used CHAID (chi-squared automatic interaction detection) analyses, a decision-tree technique based on information reported over a 13-year period. Findings: The CHAID analyses indicated that national government departments’ corporate governance success is primarily explained by three factors, namely: a quality internal audit function (main predictor), the number of fraud incidents, and the number of internal control weaknesses. Implication: Even though all the variables in this study receive attention in literature and regulation, the problem of unfavourable audit outcomes persists. The question is where to focus on resolving the problem. Regulators may take note of where to focus their efforts in strengthening elements of good corporate governance.