Tree Method Research Articles

Enhancing breast cancer diagnosis: a comparative analysis of feature selection techniques

Breast cancer is a significant contributor to female mortality, emphasizing the importance of early detection. Predicting breast cancer accurately remains a complex challenge within medical data analysis. Machine learning (ML) algorithms offer valuable assistance in decision-making and diagnosis using medical data. Numerous research studies highlight the effectiveness of ML techniques in improving breast cancer prediction. Feature selection plays a pivotal role in data preprocessing, eliminating irrelevant and redundant features to minimize feature count and improve classification accuracy. This study focuses on optimizing breast cancer diagnostics through feature selection methods, specifically genetic algorithms (GA) and particle swarm optimization (PSO). The research involves a comparative analysis of these methods and the application of a diverse set of ML classification techniques, including logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble methods like random forest (RF), AdaBoost, and gradient boosting (GB), using a breast cancer dataset. The models' performance is subsequently evaluated using various performance metrics. The experimental findings illustrate that PSO achieved the highest average accuracy, reaching 99.6% when applied to AdaBoost, while GA attained an accuracy rate of 99.5% when employed with both AdaBoost and RF.

A preordonance-based decision tree method and its parallel implementation in the framework of Map-Reduce

In supervised classification, decision trees are one of the most popular learning algorithms that are employed in many practical applications because of their simplicity, adaptability, and other perks. The development of effective and efficient decision trees remains a major focus in machine learning. Therefore, the scientific literature provides various node splitting measurements that can be utilized to produce different decision trees, including Information Gain, Gain Ratio, Average Gain, and Gini Index. This research paper presents a new node splitting metric that is based on preordonance theory. The primary benefit of the new split criterion is its ability to deal with categorical or numerical attributes directly without discretization. Consequently, the Preordonance-based decision tree” (P-Tree) approach, a powerful technique that generates decision trees using the suggested node splitting measure, is developed. Both multiclass classification problems and imbalanced data sets can be handled by the P-Tree decision tree strategy. Moreover, the over-partitioning problem is addressed by the P-Tree methodology, which introduces a threshold ϵ as a stopping condition. If the percentage of instances in a node falls below the predetermined threshold, the expansion of the tree will be halted. The performance of the P-Tree procedure is evaluated on fourteen benchmark data sets with different sizes and contrasted with that of five already existing decision tree methods using a variety of evaluation metrics. The results of the experiments demonstrate that the P-Tree model performs admirably across all of the tested data sets and that it is comparable to the other five decision tree algorithms overall. On the other hand, an ensemble technique called “ensemble P-Tree” offers a reliable remedy to mitigate the instability that is frequently associated with tree-based algorithms. This ensemble method leverages the strengths of the P-Tree approach to enhance predictive performance through collective decision-making. The ensemble P-Tree strategy is comprehensively evaluated by comparing its performance to that of two top-performing ensemble decision tree methodologies. The experimental findings highlight its exceptional performance and competitiveness against other decision tree procedures. Despite the excellent performance of the P-Tree approach, there are still some obstacles that prevent it from handling larger data sets, such as memory restrictions, time complexity, or data complexity. However, parallel computing is effective in resolving this kind of problem. Hence, the MR-P-Tree decision tree technique, a parallel implementation of the P-Tree strategy in the Map-Reduce framework, is further designed. The three parallel procedures MR-SA-S, MR-SP-S, and MR-S-DS for choosing the optimal splitting attributes, choosing the optimal splitting points, and dividing the training data set in parallel, respectively, are the primary basis of the MR-P-Tree methodology. Furthermore, several experimental studies are carried out on ten additional data sets to illustrate the viability of the MR-P-Tree technique and its strong parallel performance.

Predicting Wall Pressure Fluctuations on Aerospace Launchers Through Machine Learning Approaches

Artificial intelligence (AI) can be used to optimize the prediction of pressure fluctuations over the external surfaces of aerospace launchers and minimize the number of wind tunnel tests. In the present research, various machine learning (ML) techniques capable of predicting the acoustic load were tested and validated. The methods included decision trees, Gaussian Process Regression (GPR), Support Vector Machines (SVMs), artificial neural networks (ANNs), linear regression, and ensemble methods such as bagged and boosted trees. These algorithms were trained using experimental data from an extensive wind tunnel test campaign conducted to support the design of a VEGA (Advanced Generation European Vehicle) launcher vehicle and provide wall pressure fluctuations in many configurations. The main objective of this study was to identify, among several algorithms, the most suitable method able to process such complex databases efficiently and to provide reliable predictions. Different statistical indices, including the root mean square error (RMSE), the mean square error (MSE), and a correlation coefficient (R-squared), were employed to evaluate the performance of the ML methods. Among all the methods, the bagged tree algorithm outperformed the others, providing the most accurate predictions, with low RMSE and high R-squared values across all test cases. Other methods, such as the ANNs and GPR, exhibited higher errors, indicating their reduced suitability for this dataset. The results demonstrate that ensemble decision tree methods are highly effective in predicting acoustic loads, offering reliable predictions, even for configurations outside the training database. These findings support the application of ML-based models to optimize experimental campaigns and enhance the design of aerospace launch vehicles.

How to Teach a Sociology Student Good Data Analiysis Using SPSS?

The paper presents the author’s thoughts and explores the process of teaching sociology students to analyze data using SPSS and the course book by N. Voronina, published in 2024. It also provides a brief overview of the available domestic learning materials in SPSS-based data analysis and argues that the genre of course books written by a new generation of researchers and teachers is of relevance. N. Voronina’s course book shows an understanding of the specifics of sociology students, as students who still have signs of avoiding information that is heavy in formulas and mathematical terminology, together with an understanding of how important is gaining analytical skills for professional activities. The author argues that this contradiction can be removed by bringing the research methodology to the forefront, while the analysis methods themselves should come second. The author’s high level of empathy towards students who study analysis and the use of 232 pictures embracing all the nuances of the applied processes can contribute to effective learning and application of the methods under consideration. The course book outlines all data processing and analysis approaches that may be required to develop the basic skills of a sociology research analyst. Deep and substantial description of regression analysis, decision tree method (CHAID), and discriminant analysis techniques are redundant in this context. Therewith, the author disregards the fact that the most common sociology research method is crosstabs, and its brief overview in the course book is insufficient.

DEVELOPMENT OF A CREDIT SCORING MODEL USING MACHINE LEARNING FOR COMMERCIAL BANKS IN VIETNAM

In an increasingly competitive financial market, developing an accurate and efficient credit scoring model is crucial for banks to make informed credit decisions, and to help customers obtain credit products that match their financial capabilities. This study aims to develop a credit scoring model for individual customers of Vietnamese banks using machine learning techniques. This article will use a set of financial and non-financial indicators as inputs for various machine learning models such as Logistic Regression, K-nearest Neighbor, Decision Tree, Random Forest, LightGBM, and Support Vector Method. The obtained results from these models will be carefully compared and evaluated to ultimately select the best credit scoring model for the bank. This study leverages Google Colab, a cloud-based platform, for comprehensive data analysis and model development, ensuring a robust and data-driven approach. This study hopes to provide a useful solution for Vietnamese banks in managing credit risk and improving their business performance.

Regression Trees With Fused Leaves.

We propose a novel regression tree method named "TreeFuL," an abbreviation for 'Tree with Fused Leaves.' TreeFuL innovatively combines recursive partitioning with fused regularization, offering a distinct approach to the conventional pruning method. One of TreeFuL's noteworthy advantages is its capacity for cross-validated amalgamation of non-neighboring terminal nodes. This is facilitated by a leaf coloring scheme that supports tree shearing and node amalgamation. As a result, TreeFuL facilitates the development of more parsimonious tree models without compromising predictive accuracy. The refined model offers enhanced interpretability, making it particularly well-suited for biomedical applications of decision trees, such as disease diagnosis and prognosis. We demonstrate the practical advantages of our proposed method through simulation studies and an analysis of data collected in an obesity study.

Risk Assessment Of Gonggang Dam In Magetan District, East Java Province

This study aims to assess the safety risk of the Gonggang Dam located in Magetan Regency, East Java, using traditional methods and the event tree method (Event Tree Analysis). The risk assessment is conducted by identifying potential hazards and failure modes, estimating the probability of failure, and evaluating the level of risk based on the analysis results. The results of the analysis show that the event tree method provides acceptable risk results, while the traditional method shows unacceptable risks. The priority of risk management at Gonggang Dam is focused on monitoring the dam body and inundation area to maintain safety and maximize the life of the dam. Recommendations were given to improve structural safety and regular monitoring.

Machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS

Objective: The objective was to establish a machine learning-based model for predicting the occurrence and mortality of nonpulmonary sepsis-associated ARDS. Methods: 80% of sepsis patients selected randomly from the MIMIC-IV database, without prior pulmonary conditions and with nonpulmonary infection sites, were used to construct prediction models through machine learning techniques (including K-nearest neighbour, extreme gradient boosting, support vector machine, deep neural network, and decision tree methods). The remaining 20% of patients were utilized to validate the model’s accuracy. Additionally, local data were employed for further model validation. Results: A total of 11,409 patients were included, with the most common type of infection being bloodstream infection. A total of 7,632 (66.9%) patients developed nonpulmonary sepsis-associated ARDS (NPS-ARDS). Patients with NPS-ARDS had significantly longer ICU stays (6.2 ± 5.2 days vs. 4.4 ± 3.7 days, p < 0.01) and higher 28-day mortality rates (19.5% vs. 14.9%, p < 0.01). Both internal and external validation demonstrated that the model constructed with the extreme gradient boosting method had high accuracy. In the internal validation, the model predicted NPS-ARDS and mortality in such patients with accuracies of 77.5% and 71.8%, respectively. In the external validation, the model predicted NPS-ARDS and mortality in these patients with accuracies of 78.0% and 81.4%, respectively. Conclusion: The model established via the extreme gradient boosting method can predict the occurrence and mortality of nonpulmonary sepsis-associated ARDS to a certain extent.

Unravelling the knowledge matrix: exploring knowledge-sharing behaviours on market-based platforms using regression tree analysis

PurposeThis study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.Design/methodology/approachThe research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.FindingsCognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.Originality/valueThis research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

ASSESSMENT OF THE PROBABILITY OF OCCURRENCE OF EMERGENCY SITUATIONS DURING OPERATION OF THE PYROLYSIS PLANT

The article discusses the issues of reliability and safety of operation of a pyrolysis plant for the disposal of used tires, which, in the context of an increasing volume of waste and the need for their effective disposal, is an urgent topic. The problem statement is related to the identification of factors affecting the reliability of the installation and the development of methods to prevent emergencies.The purpose of the study is to analyz the reliability of the pyrolysis plant and develop recommendations for its improvement. To achieve this goal, the causes and probabilities of failures of system elements are investigated, an analysis using the failure tree method is carried out. This method made it possible to identify the weaknesses of the system and assess the probability of their failures, which is critical to prevent emergencies.An assessment of the reliability of the installation showed that the greatest contribution to the probability of accidents is made by a malfunction of the gas separation system. This element is the most vulnerable due to its complexity and importance in the pyrolysis process, as it is responsible for the safe and effective separation of pyrolysis products. Any malfunction of the gas separation system can lead to the accumulation of dangerous gases, increasing the risk of explosions and fires. The identified emergencies require special attention to the development of effective measures to minimize them.The article proposes methods to improve the reliability of the system, the main of which is the redundancy of the most vulnerable elements. This will significantly reduce the probability of failures and increase the overall reliability of the installation. In addition, it is recommended to carry out regular inspections and maintenance of all elements of the system. These measures will help to identify and eliminate potential malfunctions in a timely manner, which will significantly reduce the likelihood of failures that can lead to emergencies.Thus, ensuring the reliability and safety of a pyrolysis plant for the disposal of used tires requires an integrated approach. Enabling redundancy of vulnerable elements and regular maintenance will minimize the likelihood of failures that can lead to emergencies and guarantee stable and safe operation of the system. These measures will not only increase the efficiency of disposal, but also ensure reliable and long-term operation of the installation.This article focuses on the importance of identifying vulnerable elements of the system and conducting regular maintenance to improve the reliability and safety of operation of the pyrolysis plant.

Cultivating Trees: Lewis Carroll’s Method of Solving (and Creating) Multi-literal Branching Sorites Problems

Abstract Lewis Carroll has been credited for developing a “Method of Trees” for solving multi-literal sorites problems, which anticipates several aspects of contemporary “tableau” or tree systems of logical proof. In particular, Carroll’s method pioneers the use of branching paths as a means of displaying or illustrating inclusive disjunction. However, rather than focusing on the respects in which Carroll’s tree diagrams resemble contemporary tree systems, I propose to focus instead on significant aspects in which they differ. In particular, I will show how the sorts of multi-literal sorites problems that Carroll’s method of trees is designed to solve are particularly ill-suited for resolution by more contemporary tree methods. I will also show how Carroll likely used something like this method, not only to solve but also to craft some of the trickier logical puzzles for which he is also famous.

Development of a recommendation engine for university study programme selection: A regression-based approach

This paper is the third in a series focused on bridging the gap between secondary and higher education. Our primary objective is to develop a robust theoretical framework for an innovative e-business model called the Undergraduate Study Programme Search System (USPSS). This system considers multiple criteria to reduce the likelihood of exam failure or the need for multiple retakes, while maximizing the chances of successful program completion. Testing of the proposed algorithm demonstrated that the Stochastic Gradient Boosted Regression Trees method outperforms the current method used in Lithuania for admitting applicants to 47 educational programs. Specifically, it is more accurate than the Probabilistic Neural Network for 25 programs, the Ensemble of Regression Trees for 24 programs, the Single Regression Tree for 18 programs, the Random Forest Regression for 16 programs, the Bayesian Additive Regression Trees for 13 programs, and the Regression by Discretization for 10 programs.

Unsupervised Decision Trees for Axis Unimodal Clustering

The use of decision trees for obtaining and representing clustering solutions is advantageous, due to their interpretability property. We propose a method called Decision Trees for Axis Unimodal Clustering (DTAUC), which constructs unsupervised binary decision trees for clustering by exploiting the concept of unimodality. Unimodality is a key property indicating the grouping behavior of data around a single density mode. Our approach is based on the notion of an axis unimodal cluster: a cluster where all features are unimodal, i.e., the set of values of each feature is unimodal as decided by a unimodality test. The proposed method follows the typical top-down splitting paradigm for building axis-aligned decision trees and aims to partition the initial dataset into axis unimodal clusters by applying thresholding on multimodal features. To determine the decision rule at each node, we propose a criterion that combines unimodality and separation. The method automatically terminates when all clusters are axis unimodal. Unlike typical decision tree methods, DTAUC does not require user-defined hyperparameters, such as maximum tree depth or the minimum number of points per leaf, except for the significance level of the unimodality test. Comparative experimental results on various synthetic and real datasets indicate the effectiveness of our method.

Spatial scale effects of transportation, social and natural attributes of street environments on perceived activity opportunities for older adults

The street environment correlates with perceived activity opportunities for older adults. While numerous studies have examined the transportation and social attributes of street environments, the natural attributes have been less explored. Furthermore, the nonlinear relationship between the street environment and perceived activity opportunities across different spatial scales remains under-researched. We hypothesize that transportation, social, and natural attributes influence perceived activity opportunities, and their nonlinear effects vary across spatial scales. Therefore, we used activity survey data and multi-source big data from Dalian, China. Employing gradient boosted decision tree (GBDT) methods, we evaluated the nonlinear correlation between the street environment and perceived activity opportunities. Our study considered three community life circle scales: 5-min, 10-min, and 15-min. The results indicated that street features varied significantly at different spatial scales. Specifically, factors such as density of street network, diversity of street interface, distance to the nearest public transport station, accessibility of green space, diversion ratio, and distance to the nearest blue space notably influence perceived activity opportunities across these scales. The study also found that the nonlinear correlations between street environments and perceived activity opportunities were prevalent and varied across spatial scales. These results suggest that priorities for street environment improvements should account for these spatial scale variations. Our research provides multi-scale recommendations for the development of sustainable transportation, age-friendly communities, and the promotion of aging in place.

A Combined Strategy for Path Planning of Tensegrity Manipulators Considering Structural Stability

Abstract A strategy combining an improved rapidly exploring random tree (RRT) method with the artificial potential field (APF) method is proposed for the path planning of flexible tensegrity manipulators considering typical complex constraints, such as structural stability, obstacle avoidance, and cable no-slackening. The relationship between the node displacements and the elongations of active members is established for the kinematic analysis of tensegrity manipulators. The rest lengths of active members are taken as the design variables for the generation of a random tree. The guide node is utilized for goal-biased samplings to expedite the exploration toward the final configuration, while the APF method is introduced to ensure that the obtained elongations of active members can satisfy the constraint of obstacle avoidance. The proportion of random and goal-biased samplings is adaptively adjusted based on the sampling success rate. Futile random samplings can be significantly reduced by dynamically modifying the random sampling range according to the obtained configuration nearest to the final configuration. The computational procedure of the proposed method is presented. An illustrative flexible tensegrity manipulator is employed to demonstrate the adaptability of the proposed path planning method to complex constraints, especially structural stability.

Topological embedding and directional feature importance in ensemble classifiers for multi-class classification

Cancer is the second leading cause of disease-related death worldwide, and machine learning-based identification of novel biomarkers is crucial for improving early detection and treatment of various cancers. A key challenge in applying machine learning to high-dimensional data is deriving important features in an interpretable manner to provide meaningful insights into the underlying biological mechanismsWe developed a class-based directional feature importance (CLIFI) metric for decision tree methods and demonstrated its use for the The Cancer Genome Atlas proteomics data. The CLIFI metric was incorporated into four algorithms, Random Forest (RF), LAtent VAriable Stochastic Ensemble of Trees (LAVASET), and Gradient Boosted Decision Trees (GBDTs), and a new extension incorporating the LAVA step into GBDTs (LAVABOOST). Both LAVA methods incorporate topological information from protein interactions into the decision function.The different models' performance in classifying 28 cancers resulted in F1-scores of 92.6% (RF), 92.0% (LAVASET), 89.3% (LAVABOOST) and 85.7% (GBDT), with no method outperforming all others for individual cancer type prediction. The CLIFI metric enables visualisation of the model's decision-making functions. The resulting CLIFI value distributions indicated heterogeneity in the expression of several proteins (MYH11, ERα, BCL2) across different cancer types (including brain glioma, breast, kidney, thyroid and prostate cancer) aligning with the original raw expression data.In conclusion, we have developed an integrated, directional feature importance metric for multi-class decision tree-based classification models that facilitates interpretable feature importance assessment. The CLIFI metric can be combined with incorporating topological information into the decision functions of models to introduce inductive bias, enhancing interpretability.

Application of machine learning in predicting health perception through military personnel's sense of empowerment.

The promotion of health and provision of care services for new recruits are issues of constant concern for military leaders and healthcare providers, as they are crucial to maintaining and operating military forces. The enhancement of military personnel's empowerment has been recognized as a core value in promoting health perception. However, the pathways between military personnel's sense of empowerment and health perception have not been thoroughly explored. The primary aim of this study is to examine the predictive power of different dimensions of empowerment (personal, interpersonal, and socio-political) on new recruits' health perception, and to further observe differences among subgroups, which will help us grasp the nuances of future health intervention measures. The research data were extracted from the "Military Career Development Study," analyzing personal empowerment data from Wave 1 (W1) and perceived health data from Wave 2 (W2) (N = 2,232). In terms of analytical methods, five ML classifiers, including Decision Tree, Random Forest, Support Vector Machine, AdaBoost, and k-Nearest Neighbors (KNN) algorithms, were used for prediction in both the full sample and subsamples (gender and socioeconomic status). Results show that among the five ML classifiers, the Decision Tree performed best overall, achieving a prediction accuracy of 95.4%. The results by gender show that the ML models perform best for both males and females with the Decision Tree and Random Forest methods. For the Decision Tree, the accuracy rates were 94.9% for males and 95.1% for females; the F1 scores were 92.9% for males and 93.2% for females. For the Random Forest, the accuracy rates were 94.9% for males and 95.4% for females; the F1 scores were 92.7% for males and 93.2% for females. Regarding SES, the Decision Tree and Random Forest methods performed best. In the SES Low group, both methods achieved a prediction accuracy of 95.6% and an F1 score of 93.7%; in the SES high group, they achieved a prediction accuracy of 95.4% and an F1 score of 93.3%. However, the contribution of different dimensions of empowerment features varied significantly among subgroups. These findings can provide important information on the differences in health perception among military personnel.

Population Structure and Selection Signatures in Chinese Indigenous Zhaotong Pigs Revealed by Whole-Genome Resequencing.

Zhaotong pig (ZTP) is a Chinese indigenous pig breed in Yunnan Province, known for its unique body shape and appearance, good meat quality, strong foraging ability, and adaptability. However, there is still a lack of research on its genome. In order to investigate the genetic diversity, population structure, and selection signatures of the breed, we conducted a comprehensive analysis by resequencing on 30 ZTPs and comparing them with genomic data from 10 Asian wild boars (AWBs). A total of 45,514,452 autosomal SNPs were detected in the 40 pigs, and 23,649,650 SNPs were retained for further analysis after filtering. The HE, HO, PN, MAF, π, and Fis values were calculated to evaluate the genetic diversity, and the results showed that ZTPs had higher genetic diversity and lower inbreeding coefficient compared with AWBs. Population structure was analyzed using NJ tree, PCA, ADMIXTURE, and LD methods. It was found that ZTPs were population independent of AWBs and had a lower LD decay compared to AWBs. Moreover, the results of the IBS genetic distance and G matrix showed that most of the individuals had large genetic distances and distant genetic relationships in ZTPs. Selection signatures were detected between ZTPs and AWBs by using two methods, FST and π ratio. Totals of 1104 selected regions and 275 candidate genes were identified. Finally, functional enrichment analysis identified some annotated genes that might affect fat deposition (NPY1R, NPY5R, and NMU), reproduction (COL3A1, COL5A2, GLRB, TAC3, and MAP3K12), growth (STAT6 and SQOR), tooth development (AMBN, ENAM, and ODAM), and immune response (MBL2, IL1A, and DNAJA3). Our results will provide a valuable basis for the future effective protection, breeding, and utilization of ZTPs.

PREDIKSI RISIKO DEMAM BERDARAH MENGGUNAKAN DECISION TREE BERDASARKAN GEJALA KLINIS DAN DATA LABORATORIUM

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.

APPLICATION OF THE DNA BARCODING METHOD FOR IDENTIFICATION AND TAXONOMIC ANALYSIS OF THE LAMIACEAE FAMILY

The Lamiaceae family is one of the largest and most important among the aromatic plants, including many species with pronounced pharmacological properties. Plants of this family, such as mint, rosemary and thyme, are widely used in traditional and modern medicine due to their therapeutic and biochemical properties. The importance of correct species identification is emphasized by the need to preserve the biodiversity and sustainable use of the resources of this family. In this work, plants of Lamiaceae family growing in the territory of the Karaganda region were studied by DNA barcoding. The studies were conducted on the basis of nucleotide sequences of rbcL and trnH-psbA chloroplast DNA barcodes using the methods of paired genetic distances, BLASTn and phylogenetic tree based on the analysis of maximum parsimony. The high efficiency of the rbcL locus was demonstrated for the identification of higher taxonomic groups, such as subfamilies and tribes, while the intergenic spacer trnH-psbAhasproved to be effectiveat the level of speciesandgenus level.