Support Vector Machine Algorithm Research Articles

Algorithms for graph theory matching problems based on computer science

Existing graph-matching algorithms only use the graph’s edges to transmit information and cannot perform hierarchical reasoning and fusion. Although they have achieved good results in graph matching, problems exist such as planarization and insufficient representation of graph data. This paper aims to study graph theory matching algorithms based on computer science, solve matching problems on large-scale graphs, reduce computation time, and improve solving efficiency. This paper studied graph theory matching algorithms based on computer science, including heuristic algorithm-based matching algorithms and machine learning-based matching algorithms; the specific matching problem was mathematically analyzed and transformed into a matching problem in graph theory, which included defining the input and output of the problem, determining the structure and properties of nodes and edges in the graph, and clarifying the required matching and constraint conditions. The experimental results of this paper showed that the average optimal matching rates of the Kuhn–Munkres algorithm, Gale–Shapley (GS)-based algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm and Support Vector Machine (SVM) algorithm in complex scenarios were 62.05%, 58.42%, 82.79%, 81.99% and 83.50%, respectively. Studying graph theory matching algorithms based on computer science is of great significance. It can not only solve practical application problems and improve computational efficiency, but also promote algorithm optimization, which helps to improve the processing speed and efficiency of practical problems.

Classification model for blast furnace status based on multi-source information

The blast furnace (BF) is the key equipment for smelting, which has a significant impact on the sustainable development of global environment and energy. Therefore, it will be helpful for the operators if accurate evaluation to the status of the BF is realized. The classification model based on multi-source information is proposed in this paper, where the kernel fisher (KF) algorithm is enhanced to simplify the complexity both in algorithm and data dimensionality. The proposed model can realize the classification of both the distribution of gas flow (GF) and the geometric information of the burden surface (BS), and this paper also proposes a dynamic process adaptive kernel fisher (DPAKF) algorithm to enhance the adaptive ability of the classifier, where the online algorithm updating mechanism is designed. The experimental results demonstrate that the classification accuracy was increased from 90% to 95%, and the time expense of DPAKF was 236 s, which is prior compared with the existing support vector machine (SVM), k-nearest neighbor (KNN) and genetic algorithm (GA) models. The results of the student's t-distribution show that the statistics between DPAKF and KF, SVM, KNN and GA were 1.25, 2.81, 2.84 and 2.96, respectively, which demonstrate that there are significant statistical differences between DPAKF and SVM, KNN, and GA. This research provides a potential solution to the classification problem of the BF.

Unsaturated Fatty Acid Profiles and Prognostic Significance in Epilepsy Patients: A Comprehensive Analysis Using UPLC-MS/MS and SVM Algorithm

Unsaturated fatty acids (UFAs) play a crucial physiological role in human body. However, the concentration-related changes and prognostic significance of UFAs in epilepsyremain unclear. An optimized ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach was developed to measure six key UFAs: oleic acid (OA), linoleic acid (LA), arachidonic acid (AA), α-linolenic acid (ALA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA). Subsequently, the levels of these six UFAs were determined in 40 healthy individuals and 49 epilepsy patients. The diagnostic value of UFAs and clinical examination indicators were assessed using statistical analysis and the support vector machine (SVM) algorithm. The results showed that the UPLC-MS/MS method successfully quantified the levels of OA, LA, AA, ALA, EPA, and DHA in both the healthy individuals and epilepsy patients. Compared with the healthy group, the levels of ALA, AA, and DHA were significantly elevated in the epilepsy group (P < 0.05). Pearson correlation analysis revealed a strong positive correlation among the UFAs in the epilepsy group. The orthogonal partial least squares-discriminant analysis (OPLS-DA) model showed that DHA and EPA were more important than cholesterol in distinguishing between two groups, although the separation was not complete. The SVM model achieved better separation, with an area under the curve (AUC) of 0.95 when including the six UFAs. The EPA/DHA ratio was identified as a key feature, with a significant contribution to the model's performance. Removing the six UFAs from the model reduced the AUC to 0.91, highlighting the predictive value of UFAs for epilepsy. In conclusion, ALA, AA, and DHA, are altered in epilepsy patients. The EPA/DHA ratio was found to be a key predictive indicator for epilepsy. The use of UFAs in conjunction with clinical examination data improved the predictive power of the SVM model, suggesting that UFAs have potential as biomarkers for epilepsy.

Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and Support Vector Machine (SVM) algorithm

Firearm violence results in thousands of victims annually in Brazil, which is ascribed, among other factors, to the low homicide resolution rates. One method for generating forensic evidence in firearm discharges is the identification of gunshot residues (GSR), typically composed of inorganic particles containing primarily lead (Pb), barium (Ba), and antimony (Sb). The “gold standard” technique for GSR identification is Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) which is highly accurate but demands significant time and labor for each sample analysis. Other tests to detect GSR include the colorimetric rhodizonate test and spectroscopic techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), which are quicker, but exhibit lower reliability and a higher potential for false positives and potentially compromise the investigation. This study introduces a novel GSR analytical protocol as a potential alternative to SEM-EDS and colorimetric tests, which is based on LIBS analysis combined with dimensionality reduction performed by PCA and probabilistic Support Vector Machine (SVM) algorithm. The developed protocol accurately predicts both positive and negative samples, and establishes the probability of each prediction, allowing for the exclusion of questionable samples from the analysis. The initial tests show the protocol achieved 100 % accuracy in both training and external validation. As a stress test, the protocol is able to exclude questionable samples from the analysis and accurately predict true samples with 100 % correct classifications from a set of artificially contaminated samples containing elements characteristic of GSR, highlighting the protocol’s significant reduction in the likelihood of false positives.

Identification of Crosstalk Genes between Lung Adenocarcinoma and Periodontitis.

Lung adenocarcinoma (LUAD) represents a significant global health issue. Smoking contributes to the development of periodontitis and LUAD. The connections between the two are still ambiguous. Based on RNA expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes (DEGs) in Periodontitis and LUAD were collected. Protein-protein interaction (PPI) networks were produced by mining genes intersecting with crossover DEGs. Genes in the subnetwork and the top 15 genes of the topology score were defined as the crosstalk gene. Feature selection and diagnostic model construction were conducted based on Recursive Feature Elimination (RFE) and support vector machines (SVM). Additionally, we analyzed the immune cells and signaling pathways influenced by the crosstalk gene. A total of 29 crossover DEGs between Periodontitis and LUAD were filtered, with 20 genes interacting with them in the PPI network. Five subnetworks with similar interaction patterns in the PPI network were detected. Based on the network topology analysis, genes ranking in the top 15 were used to take the intersection with those genes in the 5 subnetworks. Twelve intersecting genes were identified. Based on RFE and SVM algorithms, FKBP11 and MMP13 were considered as the Crosstalk genes for both Periodontitis and LUAD. The diagnostic model composed of FKBP11 and MMP13 showed excellent diagnostic potential. In addition, we found that FKBP11 and MMP13 influenced Macrophages, M1, T cells, CD8 activity, immune-related pathways, and cell cycle pathways. We identified the crosstalk genes (FKBP11 and MMP13) between periodontitis and LUAD. The two genes affected the comorbidity status between the two diseases through immune cell activity.

Free vibration behaviour of bio-inspired helicoidal laminated composite panels of revolution under thermal conditions: Multi-output machine learning approach

The present work aims to study the free vibration behaviour of bio-inspired helicoidal laminated composite spherical, toroid, and conical shell panels using a single-output Support Vector Machine (SVM) algorithm trained in the chassis of parabolic shear deformation theory under thermal conditions. Different helicoidal lamination schemes are adopted, such as Fibonacci, semi-circular, exponential, recursive, and linear helicoidal schemes. Temperature-dependent material properties are adopted. The effect of the geometry of the shell, temperature, and lamination scheme on the free vibration behaviour of spherical, toroid, and conical shell panels is studied. Also, the mode shapes are obtained using different multi-output SVM surrogate in which the displacements are obtained at different locations and are predicted to obtain the fundamental mode shape. The trained surrogate model can predict the values of fundamental frequency and mode shapes much faster than the parabolic shear deformation theory.

Hyperparameter recommendation via automated meta-feature selection embedded with kernel group Lasso learning

Hyperparameter recommendation via meta-learning relies on the characterization and quality of meta-features. These meta-features provide critical information about the underlying datasets but are often selected manually based on the practitioner’s experience and preference, which can be inefficient and ineffective in many applications. In this paper, we propose a novel hyperparameter recommendation approach that integrates with a Lasso-based multivariate kernel group (KGLasso) model. The developed KGLasso model automatically identifies primary meta-features through model training. By selecting the most explanatory meta-features for a specific meta-learning task, the recommendation performance becomes much more effective. Our KGLasso model builds on a group-wise generalized multivariate Lasso approach. Within this framework, we establish a minimization algorithm using a corresponding auxiliary function, which is mathematically proven to be convergent and robust. As an application, we develop a hyperparameter recommendation system using our built KGLasso model on 120 UCI datasets for the well-known support vector machine (SVM) algorithm. This system efficiently provides competent hyperparameter recommendations for new tasks. Extensive experiments, including comparisons with popular meta-learning baselines and search algorithms, demonstrate the superiority of our proposed approach. Our results highlight the benefits of integrating model learning and feature selection to construct an automated meta-learner for hyperparameter recommendation in meta-learning.

Intuitionistic Fuzzy SVM based on Kernel Gray Relational Analysis

Fuzzy Support Vector Machine (FSVM) is a machine learning algorithm that combines fuzzy logic with Support Vector Machine (SVM) to deal with the uncertainty and fuzziness in classification and regression problems. This algorithm improves the performance of traditional SVM by introducing fuzzy membership degrees, making it more robust when handling datasets with noise or uncertainty. Although the existing FSVM algorithms can overcome the influence of noise to a certain extent, they cannot effectively distinguish outliers or abnormal values from boundary support vectors. To solve this problem, this study proposes an Intuitionistic Fuzzy Support Vector Machine algorithm (KGRA-IFSVM) based on Kernel Grey Relational Analysis (KGRA). This approach utilizes gray relational analysis in the kernel space to calculate the gray relational degree between each sample and its K isomorphic neighboring points, and takes the average value as the membership degree of the sample. Then, the same approach is used to compute the gray relational degree between each sample and its K heterogeneous neighboring points, and the average value is taken as the non-membership degree of the sample. Finally, each sample is assigned with an appropriate fuzzy value based on intuitionistic fuzzy sets using a specific scoring function. Test results on UCI datasets show that KGRA-IFSVM has better classification performance and stronger noise resistance.

ACO-Guided Genetic Analysis: Exploring the Role of Genes in Alzheimer's Disease

Alzheimer's disease (AD), a neurodegenerative disorder significantly impairing cognitive function and quality of life, necessitates an in-depth study of its pathogenic mechanisms. Despite extensive research, the disease's pathological mechanisms harbor numerous unknown elements. Analyzing DNA microarray data is crucial for elucidating gene co-expression relationships during AD's pathological processes. This study aimed at exploring the applicability of the ant colony optimization (ACO) algorithm in identifying linearly co-expressed genes in AD. This study utilized gene expression profile data of AD patients collected from the Gene Expression Omnibus (GEO) database, employing the ACO algorithm to explore the co-expression relationships among AD pathogenic genes in different stages. Through the analysis of co-expression relationships, we identified four related genes (SDHA, NDUFA10, SDHC, and GPI). By using the co-expressed genes as features, we applied the support vector machine (SVM) algorithm for AD classification. The results of these experiments revealed that each model achieved average AUC values of 0.90, 0.91, 0.86, and 0.92, respectively. Our research findings reveal that the ACO algorithm provides a new perspective for in-depth exploration of the pathogenic mechanisms of AD.

Web Application for Diabetes Prediction using Machine Learning Techniques

The objective of this project is to predict a person's risk of having diabetes by utilizing Support Vector Machine (SVM) algorithms in an intuitive web application interface. This application attempts to provide accurate and reasonable predictions by using input health parameters (number of pregnancies, blood pressure, glucose level, insulin level, age, skin thickness, diabetes pedigree function, etc.) that users provide via a graphical user interface (GUI). By combining the power of SVM with user-friendly web technology, the project endeavors to enhance accessibility to predictive healthcare tools. The seamless integration of Machine Learning into a web application facilitates a simple and effective method for diabetes prediction, which could aid people in making accurate choices regarding their health. By promoting preventive measures and giving people early awareness, this initiative hopes to support proactive healthcare.

Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques

BackgroundSub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine learning techniques and to explore factors influencing women's choices of delivery locations in East Africa.MethodThe study focused on 86,009 childbearing women in East Africa. A comparative analysis of 12 advanced machine learning algorithms was conducted, utilizing various data balancing techniques and hyperparameter optimization methods to enhance model performance.ResultThe prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant difference between them in all comprehensive analysis of metrics performance. Factors associated with facility-based deliveries were identified using association rule mining, including parental education levels, timing of initial antenatal care (ANC) check-ups, wealth status, marital status, mobile phone ownership, religious affiliation, media accessibility, and birth order.ConclusionThis study underscores the vital role of machine learning algorithms in predicting health facility deliveries. A slight decline in facility deliveries from previous reports highlights the urgent need for targeted interventions to meet Sustainable Development Goals (SDGs), particularly in maternal health. The study recommends promoting facility-based deliveries. These include raising awareness about skilled birth attendance, encouraging early ANC check-up, addressing financial barriers through targeted support programs, implementing culturally sensitive interventions, utilizing media campaigns, and mobile health initiatives. Design specific interventions tailored to the birth order of the child, recognizing that mothers may have different informational needs depending on whether it is their first or subsequent delivery. Furthermore, we recommended researchers to explore a variety of techniques and validate findings using more recent data.

Caffeine Content Prediction in Coffee Beans Using Hyperspectral Reflectance and Machine Learning

The application of hyperspectral data in machine learning models can contribute to the rapid and accurate determination of caffeine content in coffee beans. This study aimed to identify the machine learning algorithm with the best performance for predicting caffeine content and to find input data for these models that can improve the accuracy of these algorithms. The coffee beans were harvested one year after the seedlings were planted. The fresh beans were taken to the spectroscopy laboratory (Laspec) at the Federal University of Mato Grosso do Sul, Chapadão do Sul campus, for spectral evaluation using a spectroradiometer. For the analysis, the dried coffee beans were ground and sieved for the quantification of caffeine, which was carried out using a liquid chromatograph on the Waters Acquity 1100 series UPLC system, with an automatic sample injector. The spectral data of the beans, as well as the spectral data of the roasted and ground coffee, were analyzed using machine learning (ML) algorithms to predict caffeine content. Four databases were used as input: the spectral information of the bean (CG), the spectral information of the bean with additional clone information (CG+C), the spectral information of the bean after roasting and grinding (CGRG) and the spectral information of the bean after roasting and grinding with additional clone information (CGRG+C). The caffeine content was used as an output to be predicted. Each database was subjected to different machine learning models: artificial neural networks (ANNs), decision tree (DT), linear regression (LR), M5P, and random forest (RF) algorithms. Pearson’s correlation coefficient, mean absolute error, and root mean square error were tested as model accuracy metrics. The support vector machine algorithm showed the best accuracy in predicting caffeine content when using hyperspectral data from roasted and ground coffee beans. This performance was significantly improved when clone information was included, allowing for an even more accurate analysis.

Machine learning-driven fluorescent sensor array using aqueous CsPbBr3 perovskite quantum dots for rapid detection and sterilization of foodborne pathogens

With the growing global concern over food safety, the rapid detection and disinfection of foodborne pathogens have become critical in public health. This study presents a novel machine learning-driven fluorescent sensor array utilizing aqueous CsPbBr3 perovskite quantum dots (PQDs) for the rapid identification and eradication of foodborne pathogens. The relative signal intensity changes (ΔRGB) generated by the sensor array were analyzed using the machine learning algorithm—Support Vector Machine (SVM). The study achieved the identification and recognition of five pathogens and their mixtures within a concentration range of 1.0 × 103 to 1.0 × 107 CFU/mL with an accuracy rate of 100 %, and the limits of detection (LOD) for the pathogens were found to be low. Additionally, the array also showed excellent performance in the identification of pathogens in tap water, achieving an accuracy rate of 100 %. Furthermore, the fluorescent sensor array was capable of inactivating the pathogens with an efficiency of over 99 % within 30 min post-detection. This development provides an efficient and reliable tool for the field of food safety detection.

A comprehensive machine learning-based models for predicting mixture toxicity of azole fungicides toward algae (Auxenochlorella pyrenoidosa)

Quantitative structure–activity relationships (QSARs) have been used to predict mixture toxicity. However, current research faces gaps in achieving accurate predictions of the mixture toxicity of azole fungicides. To address this gap, the application of machine learning (ML) algorithms has emerged as an effective strategy. In this study, we applied 12 algorithms, namely, k-nearest neighbor (KNN), kernel k-nearest neighbors (KKNN), support vector machine (SVM), random forest (RF), stochastic gradient boosting (GBM), cubist, bagged multivariate adaptive regression splines (Bagged MARS), eXtreme gradient boosting (XGBoost), boosted generalized linear model (GLMBoost), boosted generalized additive model (GAMBoost), bayesian regularized neural networks (BRNN), and recursive partitioning and regression trees (CART) to build ML models for 225 mixture toxicity of azole fungicides towards Auxenochlorella pyrenoidosa. A total of 36 single ML models and 12 consensus models were developed. The results indicated that models employing concentration addition (CA), independent action (IA), and molecular descriptors (MD) as variables demonstrated superior predictive abilities. The consensus model combining SVM and RF algorithms (labeled as CM0) demonstrated the highest level of accuracy in fitting the data, with a coefficient of determination of 0.980. Additionally, it showed strong predictive abilities when tested with external data, achieving an external R2 value of 0.945 and a Concordance Correlation Coefficient of 0.967. This study provides a positive contribution to the ecological risk assessment of a mixture of azole fungicides.

Heart Disease Prediction using Machine Learning Techniques

Throughout their growth, artificial intelligence and machine learning have shown beneficial in multiple areas, particularly with the huge amount of data that has been generated recently. Making quicker and more accurate decisions regarding illness forecasts may be more dependable. The model can be used in the visualization and analysis of diseases. The article compares between different machine learning algorithms. In addition to numerous machine learning techniques, the UCI dataset is employed. Testing was done on Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest and Decision Tree algorithms. After performing the mentioned algorithm, it indicates that the decision tree performs better than another algorithm, its an accuracy of 98.54%.

Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning

Objectives: Cerebral edema, a prevalent consequence of brain injury, is associated with significant mortality and disability. Timely diagnosis and monitoring are crucial for patient prognosis. There is a pressing clinical demand for a real-time, non-invasive cerebral edema monitoring method. Ultrasound methods are prime candidates for such investigations due to their non-invasive nature. Methods: Acute cerebral edema was introduced in rats by permanently occluding the left middle cerebral artery (MCA). Ultrasonic echo signals were collected at nine time points over a 24 h period to extract features from both the time and frequency domains. Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. Results: The integration of 16 ultrasonic features associated with cerebral edema development with the RF model enabled effective classification of cerebral edema types, with a high accuracy rate of 97.9%. Additionally, it provided an accurate prediction of the cerebral infarction volume ratio, with an R2 value of 0.8814. Conclusions: Our proposed strategy classifies cerebral edema and predicts the cerebral infarction volume ratio with satisfactory precision. The fusion of ultrasound echo features with machine learning presents a promising non-invasive approach for the monitoring of cerebral edema.

Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning.

Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This study leverages machine learning (ML) to enhance the understanding of disease progression and uncover gender-based differences in MS by analyzing clinical data integrated with patient-reported outcomes (PROMs). We conducted a prospective cohort study involving 250 MS patients at a secondary care hospital in Spain over an 18-month period. Using REDCap for data management, we collected comprehensive demographic, clinical, and PROMs data. Our analysis utilized Decision Trees, Random Forest, and Support Vector Machine algorithms to classify patients based on disease evolution and infer Expanded Disability Status Scale (EDSS) levels. Additionally, we employed propensity score matching to analyze gender differences, focusing on clinical outcomes and quality of life measures. The results could indicate that integrating diverse data sets through ML would significantly improve the diagnostic accuracy and serve as a support for clinician's decision making. Our models achieved high accuracy in classifying MS types and predicting disability levels, demonstrating the potential of ML in personalized treatment planning. Furthermore, our findings suggest notable gender differences in disease progression and response to treatment. These insights advocate for a gender-specific approach in MS management and highlight the importance of personalized medicine. This study underscores the transformative potential of ML in enhancing the understanding and management of MS through integrated data analysis.

ANALISIS SENTIMEN PENGGUNA PADA APLIKASI BANK DIGITAL KROM DENGAN ALGORITMA SUPPORT VECTOR MACHINE

The rapid development of financial technology has driven the widespread adoption of digital banking applications, including the KROM app, among the public. This study aims to analyze user sentiment toward the KROM Digital Bank application using the Support Vector Machine (SVM) algorithm. User review data was collected from Google Play, then processed through data preprocessing steps such as text cleaning, tokenization, and removing irrelevant words. The SVM algorithm is used to classify user sentiment into positive and negative categories. The results indicate that SVM performs well in classifying user sentiment, with an accuracy of 84,38%. This analysis is expected to provide insights for app developers to improve service quality based on user perceptions and experiences.

UNLEASHING THE POWER OF SVM AND KNN: ENHANCED EARLY DETECTION OF HEART DISEASE

Heart disease is a fatal illness responsible for approximately 36% of deaths in 2020. Therefore, it is important to pay attention to and better anticipate the risk of heart disease. One technological contribution that can be made is through information related to the risk of heart disease. Classification techniques in data mining can be used to diagnose and identify the risk of heart disease earlier by processing medical data and making predictions. This study compares the effectiveness of two classification algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), in predicting the risk of heart disease using a Kaggle dataset consisting of 303 records with 14 attribute columns. The data is divided into 70% for training and 30% for testing. The software used in this study is Orange Data Mining to build the SVM and KNN models. The results show that the SVM accuracy is 85.6%, while KNN achieves 81.1%. Based on the confusion matrix, the SVM algorithm has a lower error rate compared to KNN. In conclusion, the SVM algorithm is superior to KNN in predicting the risk of heart disease. These findings indicate that SVM has a better potential in identifying individuals at high risk of experiencing a heart attack. This research can contribute to the development of a more accurate medical decision support system for early detection of heart disease.

Performance of the Machine Learning Algorithm in Stature Estimation Using Scapula Measurements from Post-Mortem Computed Tomography in a Thai Male Population

Objective: This study attempted to investigate the performance of stature prediction models from scapular dimensions based on post-mortem computed tomography (PMCT) using machine learning algorithms within the male population of Southern Thailand.Material and Methods: Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest tree (RF), and Support vector machine (SVM) algorithms were used to create the stature estimation model. Then its performance was compared by coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).Results: Machine learning is a valuable tool for estimating stature within this demographic. LR algorithm provided the best performance matrices, with the highest R2 being 0.316, and the lowest values for MAE, MSE, and RMSE being 4.379 cm, 29.530 cm, and 5.382 cm, respectively.Conclusion: The machine learning algorithm demonstrated valuable tools for estimation stature. However, it is essential to note that complex machine learning models do not always produce better performance measures than non-complex models.