Application Of Machine Learning Algorithms Research Articles

Evaluating the Performance and Challenges of Machine Learning Models in Network Anomaly Detection

The application of machine learning algorithms for anomaly detection in network traffic data is examined in this study. Using a collection of network flow records that includes attributes such as IP addresses, ports, protocols, and timestamps, the study makes use of correlation heatmaps, box plots, and data visualization to identify trends in numerical characteristics. After preprocessing, which includes timestamp conversion to Unix format, three machine learning models Support Vector Machine (SVM), Gaussian Naive Bayes, and Random Forest are used for anomaly identification. The Random Forest Classifier outperforms SVM and Naive Bayes classifiers with better precision and recall for anomaly diagnosis, achieving an accuracy of 87%. Confusion matrices and classification reports are used to evaluate the models, and they show that the Random Forest Classifier performs better than the other models in identifying abnormalities in network traffic. These results provide significant value to the field of cybersecurity by highlighting the effectiveness of machine learning models specifically, the Random Forest Classifier in boosting anomaly detection capacities for network environment security.

Dynamic Playlist Generation using Machine Learning

This study investigates the application of machine learning algorithms for dynamic playlist generation in the context of music recommendation systems. The traditional method of creating playlists frequently depends on immutable standards, like artist or genre, which might not adequately represent the unique and dynamic character of personal preferences. On the other hand, our suggested system makes use of machine learning methods to examine user behavior, preferences, and contextual elements in order to create playlists that are dynamically created and customized to each user’s individual preferences. In order to train machine learning models, the study collects user interaction data, such as listening history, skip patterns, and user feedback. To extract patterns and relationships from the data, a variety of algorithms are used, including hybrid models, content-based filtering, and collaborative filtering. After that, the models are incorporated into a dynamic playlist creation system that can eventually adjust to changing user preferences.Our test findings show how well the suggested strategy works to improve user experience by offering more interesting and customized playlists. With its ability to adjust to changing user preferences and contextual cues, the dynamic playlist generation system provides a smooth and pleasurable music discovery experience. We also talk about possible enhancements, implementation difficulties, and deployment considerations in the real world.This work adds to the ongoing efforts to improve music recommendation systems by demonstrating how machine learning can be used to develop more responsive and intelligent playlist generation systems. The results highlight how crucial customized experiences are in the constantly changing world of digital music consumption. Index Terms—hybrid mode, content-based filtering, intelligent playlist, music

Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens

The identification and characterization of immunogenic tumor antigens are essential for cancer vaccine development. In light of the impracticality of isolating and evaluating each putative antigen individually, in silico prediction algorithms, particularly those utilizing machine learning (ML) approaches, play a pivotal role. These algorithms significantly reduce the experimental workload necessary for discovering vaccine candidates. In this study, we employed six supervised ML methods on a dataset comprising 212 experimentally validated human tumor peptide antigens and an equal number of non-antigenic human peptides to develop models for immunogenicity prediction. These methods encompassed k-nearest neighbor (kNN), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). The models underwent validation through internal cross-validation within 10 groups from the training set and were further assessed using an external test set. Remarkably, the kNN model demonstrated superior performance, recognizing 90% of the known immunogens in the test set. The RF model excelled in the identification of non-immunogens, accurately classifying 93% of them in the test set. The three top-performing ML models according to multiple evaluation metrics (SVM, RF, and XGBoost) are to be subsequently integrated into the new version of the VaxiJen server, facilitating tumor antigen prediction through a majority voting mechanism.

Predicting Indian GDP with Machine Learning: A Comparison of Regression Models

In recent years, the application of machine learning algorithms in economic forecasting has gained significant traction. This study focuses on predicting the Gross Domestic Product (GDP) of India by harnessing advanced machine learning techniques. We assembled a comprehensive dataset incorporating time series analysis and inflation rates from diverse sources. Our investigation involved a comparative analysis employing both linear and polynomial regression methods to discern the most accurate predictive model. Our findings underscore the superiority of the polynomial regression model, which excelled in capturing non-linear relationships between independent variables and GDP. Specifically, the polynomial regression model achieved an impressive prediction accuracy rate of surpassing the accuracy achieved by the linear regression model. This study underscores the critical role of advanced machine learning algorithms in economic forecasting. By emphasizing the significance of high-quality datasets and the application of techniques like polynomial regression, we illustrate how these factors can substantially enhance the precision of economic forecasts. These insights hold substantial implications for policy makers and businesses alike, aiding informed decision-making and effective economic policy development. This study serves as a valuable reference for future research, highlighting the importance of advanced machine learning methods and robust data sources in economic forecasting.

Machine Learning Classification Algorithms for Traffic Stops—A Comparative Study

The application of machine learning algorithms across various fields is gaining momentum, and the results increasingly emphasize the need for further testing and implementation. This is driven by the potential to streamline and expedite numerous processes. In this paper, we have employed five algorithms: KNN, Decision Tree, Random Forest, Logistic Regression, and Naive Bayes, and these algorithms have been tested in three large datasets. On average, their performance ranges from a minimum of 80% to a maximum of 90%. Data preprocessing has been completed, and concurrently, we have implemented the SMOTE algorithm to address the challenge of unbalanced data in this research. Simultaneously, the Naïve Bayes algorithm yields the most favorable results of Accuracy, Precision, Recall, and F1 Score, for the “is_arrested” class. Furthermore, to assess the performance of each algorithm, we employed metrics including Accuracy, Precision, Recall, and F1 Score. These metrics allowed us to decide which algorithm achieved the most effective classification.

Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs

Geothermal energy is a sustainable energy source that meets the needs of the climate crisis and global warming caused by fossil fuel burning. Geothermal resources are found in complex geological settings, with faults and interconnected networks of fractures acting as pathways for fluid circulation. Identifying faults and fractures is an essential component of exploiting geothermal resources. However, accurately predicting fractures without high-resolution geophysical logs (e.g., image logs) and well-core samples is challenging. Soft computing techniques, such as machine learning, make it possible to map fracture networks at a finer resolution. This study employed four supervised machine learning techniques (multilayer perceptron (MLP), random forests (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR)) to identify fractures in geothermal carbonate reservoirs in the sub-basins of East China. The models were trained and tested on a diverse well-logging dataset collected at the field scale. A comparison of the predicted results revealed that XGBoost with optimized hyperparameters and data division achieved the best performance than RF, MLP, and SVR with RMSE = 0.02 and R2 = 0.92. The Q-learning algorithm outperformed grid search, Bayesian, and ant colony optimizations. The blind well test demonstrates that it is possible to accurately identify fractures by applying machine learning algorithms to standard well logs. In addition, the comparative analysis indicates that XGBoost was able to handle the complex relationship between input parameters (e.g., DTP > RD > DEN > GR > CAL > RS > U > CNL) and fracture in geologically complex geothermal carbonate reservoirs. Furthermore, comparing the XGBoost model with previous studies proved superior in training and testing. This study suggests that XGBoost with Q-learning-based optimized hyperparameters and data division is a suitable algorithm for identifying fractures using well-log data to explore complex geothermal systems in carbonate rocks.Graphical abstract

NEW STRATEGIES FOR IMPROVING NETWORK SECURITY AGAINST CYBER ATTACK BASED ON INTELLIGENT ALGORITHMS

Gradually, since the number of linked computer systems that use networks linked to the Internet is raised the information that is delivered through those systems becomes more vulnerable to cyber threats. This article presents proposed algorithms based on Machine Learning (ML) that ensure early detection of cyber threats that cause network breaking through the use of the Correlation Ranking Filter feature selection method. These proposed algorithms were applied to the Multi-Step Cyber-Attack Dataset (MSCAD) which consists of 66 features. The proposed strategy will apply machine learning algorithms like Adaptive Boosting-Deep Learning (AdaBoost-Deep Learning) or (ABDL), Multi-Layer Perceptron (MLP), Bayesian Networks Model (BNM), and Random Forest (RF), the feature would be decreased to high valuable of 46 features were included with a threshold of 0.1 or higher. The accuracy would be increased when the no. of features decreased to 46 with a threshold of ≥ 0.1 with the ABDL algorithm producing an accuracy of 99.7076%. The obtained results showed that the proposed algorithm delivered a suitable accuracy of 99.6791% with the ABDL algorithm even with a higher number of features.

HYBRID MODEL AND FRAMEWORK FOR PREDICTING AIR POLLUTANTS IN SMART CITIES

The pollution index of any urban area is indicated by its air quality. It also shows a fine balance is maintained between the needs of the populace and the industrial ecosystem. To mitigate such pollution in real-time, smart cities have a significant role to play. It's common knowledge that air pollution in a city severely affects the health of its dependents. More alarmingly, human health damage and disease burden are caused by phenomena like acid rain, and global warming. More precisely, lung ailments, CPOD, heart problems and skin cancer are caused by polluted air in congested urban places. Amongst the worst air pollutants, CO, C6H6, SO2, NO2, O3, RSPM/PM10, and PM2.5 cause maximum havoc. The climatic variables like atmospheric wind velocity, direction, relative humidity, and temperature control air contaminants in the air. Lately, numerous techniques have been applied by researchers and environmentalists to determine the Air Quality Index over a place. However, not a single technique has found acceptance from all quarters as being effective in every situation or scenario. Here, the main aspect relates to achieving authentic prediction in AQI levels by applying Machine Learning algorithms so worst situations can be averted by timely action. To enhance the performance of Machine Learning methods study adopted imputation and feature selection methods. When feature selection is applied, the experimental outcomes indicate a more accurate prediction over other techniques, showing promise for the application of the model in smart cities by syncing data from different monitoring stations.

Student Performance Prediction Using Machine Learning Algorithms

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university’s ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students’ performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies’ Bouldin method, obtains clusters to find important features affecting students’ performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model’s prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features.

Fraud Detection and Prevention in E-commerce using Decision Tree Algorithm

Abstract: Fraud detection is an important part of e-commerce because it helps prevent fraud such as illegal transactions, identity theft, and money laundering. Recently, there has been a lot of literature on the application of machine learning algorithms to identify e-commerce fraud. These algorithms work by learning patterns in data that indicate fraud. Pattern checking deals with discovering differences in data, such as unusual products, locations, or behavior outside the norm for certain users, through machine learning. In this project, we propose a decision tree algorithm to detect fraud in e-commerce using newly generated data from various online products on e-commerce sites. In addition to fraud detection, we also provide advice on fraud prevention. We propose a new security model that will prove the user's identity. In this security model, users are required to register their profile with some questions. Our security systems will display relevant images in response to the registration question. The user has to click on the correct answer image within the time limit. We will ask the user 3 questions in graphic format. If the user selects the correct answer, the user will be considered a real user.

УПРОВАДЖЕННЯ СИСТЕМ УПРАВЛІННЯ НАВЧАННЯМ З ФУНКЦІЯМИ ГЕНЕРАТИВНОГО ШТУЧНОГО ІНТЕЛЕКТУ В УМОВАХ ПОСТ-ПАНДЕМІЇ

In recent years, the global education system has undergone a transformative shift, relying increasingly on online tools to prove its resilience in the face of various challenges, including epidemiological events. This paper seeks to introduce a Learning Management System (LMS) designed to elevate the delivery of educational content, fostering the training and development of students' knowledge in a dynamic and adaptive learning environment anchored to the connectivity and synchronization needs of smart urban living. The system proposes the integration of a language generation model equipped with Application Programming Interface (API) access. This choice not only streamlines instructor support but also facilitates the creation of courses using an adapted and coherent language. The system goes beyond traditional LMS functionalities by providing suggestions to enhance and diversify lessons. Emphasizing flexibility and adaptability, the proposed LMS caters to various training levels, being able to accommodate the needs of learners across different domains. To identify the appropriate solution, a comparative analysis was conducted, evaluating various platforms based on functionality, intuitive presentation, and customization options. The selected system emerged as the most favorable, offering a robust framework for the development and delivery of educational content. To demonstrate the system's effectiveness, a curriculum was crafted for a specialized field of study - Artificial Intelligence (AI), with a specific focus on the practical application of Machine Learning algorithms. This curriculum incorporates theoretical and practical application components, complemented by a suite of assessment tools and assignments tailored to the proposed subject. The lessons within this curriculum were constructed by drawing insights from various bibliographical sources. What sets this system up-to-date is its integration of generative AI features directly into the LMS, enriching the teaching-learning-evaluating experience.

Customer Retention Analysis for Telecom Industry

Abstract: In today’s highly competitive telecom industry, retaining customers is vital for sustaining business growth and profitability. Customer churn, the phenomenon where customers switch from one service provider to another, poses a significant challenge for telecom companies. Predicting churn can help these companies take proactive measures to retain valuable customers. This study explores the application of machine learning algorithms for predicting customer churn in the telecom industry. Additionally, the research contributes to the existing body of knowledge in the field of customer churn prediction, showcasing the potential of machine learning algorithms in addressing complex business challenges.

Multiple Disease Prediction System Using ML

Abstract: The increasing prevalence of diverse diseases presents a challenge to global healthcare systems, underscoring the need for innovative and efficient methods for early detection and preventive measures. This paper explores the application of machine learning algorithms in multiple disease prediction to enhance diagnostic accuracy and enable timely intervention. Leveraging diverse health-related data sources, including medical records and genomic information, comprehensive predictive models are developed. A multi-faceted machine learning approach integrates support vector machines, decision trees, neural networks, and ensemble learning methods to analyze complex data patterns. Feature selection and dimensionality reduction techniques optimize model performance and interpretability. The development of a predictive system involves essential steps such as data collection, preprocessing, and model training, followed by evaluation using metrics like accuracy and recall. Integration of Flask for web application development facilitates user interaction and prediction functionality. Deployment, testing, debugging, and ongoing maintenance ensure system efficiency and compliance with regulatory requirements for healthcare data security and privacy.

Towards Early Intervention: Detecting Parkinson's Disease through Voice Analysis with Machine Learning

Introduction/ Background This study aims to utilize machine learning algorithms for early detection of Parkinson's Disease (PD) through voice recording analysis. Employing advanced machine learning techniques and a comprehensive dataset of voice samples, the objective is to develop a non-invasive, accurate, and reliable method for PD diagnosis, contributing to early intervention and management of the disease. Parkinson's Disease (PD) is a prevalent neurodegenerative disorder impacting millions globally. Early and accurate diagnosis is crucial for effective management and treatment. This study leverages Machine Learning (ML) algorithms to analyze voice recordings, aiming to improve PD detection. Materials and Methods We utilized a dataset of 195 voice samples with 23 attributes, applying machine learning algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN). The dataset was preprocessed, balanced, and evaluated using various performance metrics. Results The K-Nearest Neighbors (KNN) model demonstrated superior performance, achieving high precision (0.96-1.00), recall (0.97-1.00), and F1-scores (0.98-0.99) for both PD and non-PD classes, demonstrating an overall accuracy of 0.98 across 59 samples. This showcases its effectiveness in PD detection via voice analysis. Discussion This research underscores the potential of ML in revolutionizing PD detection through non-invasive methods. By comparing various algorithms, the study not only identifies the most effective model but also contributes to the broader understanding of applying ML techniques in healthcare. Conclusion The study's findings advocate for the KNN model as a promising tool for early and accurate PD diagnosis through voice analysis. The success of this model opens avenues for future research, including the exploration of more advanced algorithms and the integration of these models into practical diagnostic applications.

Driver Drowsiness Detection and Alert System

Abstract: Driver drowsiness is a significant factor in road accidents, necessitating the development of effective detection and alert systems to mitigate this risk. This paper provides a comprehensive review of driver drowsiness detection and alert systems, examining the technologies, methodologies, and challenges associated with these systems. Our goal is to provide an interface where the program can automatically detect the driver's drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver's sleep. Basically, you're collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not. Despite advancements, challenges remain in achieving high accuracy and minimizing false alarms. Future directions include improving sensor technology, enhancing algorithm robustness, and addressing user acceptance issues. Overall, driver drowsiness detection and alert systems play a crucial role in enhancing road safety and warrant continued research and development efforts.

Multiple Disease Prediction using ML

Abstract: This study proposes a multi-disease prediction system integrating assessments for diabetes, heart disease, and Parkinson’s disease. The system leverages machine learning algorithms like Logistic Regression, Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN) to predict disease risk through a unified user interface. We delve into the system design process, emphasizing the importance of defining system compo- nents and exploring the utility of modeling languages. The research explores the potential of SVMs, including linear and non-linear variations, for disease prediction. We analyze existing literature on applying machine learning algorithms for disease prediction and discuss their potential for disease classification. Finally, the abstract addresses challenges and future directions in disease prediction, aiming to provide valuable insights for further research and development efforts.

Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe

CONTEXTAccurate estimating of crop yield is crucial for developing effective global food security strategies which can lead to reduce of hunger and more sustainable development. However, predicting crop yields is a complex task as it requires frequent monitoring of many weather and socio-economic factors over an extended period. Satellite remote sensing products have become a reliable source for climate-based variables. They are easier to obtain and provide detailed spatial and temporal coverage. OBJECTIVEThe aim of this study is to assess the effectiveness of implement a novel optimization algorithm, called Randomized Search cross validation (RScv), on various machine learning algorithms and measure the prediction accuracy enhancement. METHODSAnnual yields of four crops (Barley, Oats, Rye, and Wheat) were predicted across 20 European countries for 20 years (2000–2019). Two NASA missions, namely GPCP and GLDAS satellites, provided us with climate- and soil-based input variables. Those variables were employed as the input of four ensemble Machine Learning (ML) algorithms (Ada-Boost (AB), Gradient Boost (GB), Random Forest (RF) and Extra Tree (ET)) which are faster and more adoptable compare to classic AI algorithms. RESULTS AND CONCLUSIONSMain results show that applying RScv improves the prediction ability of all ML models over the four crops. In particular, the RScv-AB reaches the overall highest accuracy for predicting yields (Rmax2=0.9). Spatial evaluation of predicting errors depicts that the proposed models were more shifted toward underestimation. An uncertainty analysis was also carried out which shows that applying ML algorithms creates higher and lowers uncertainty in Barley and Wheat respectively. SIGNIFICANCEConsidering the robustness of the optimised ML models and the global coverage of remote sensing data, our current methodology demonstrates great transferability and can be applied in other regions across the globe with higher temporal extents. In addition, this tool could be beneficial to decision makers in various sectors to improve the water allocations, deal with climate change effects and keep sustainable agricultural development.

Implementing Machine Learning Algorithms for Predictive Network Maintenance in 5G and Beyond Networks

With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.

Greedy Weighted Stacking of Machine Learning Models for Optimizing Dam Deformation Prediction

Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA’s expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety.

Application of machine learning algorithms to identify people with low bone density.

Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis. This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data. Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance. A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase. The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.