Performance Of Machine Learning Algorithms Research Articles

Evaluating the Performance of Machine Learning Algorithm for Classification of Safer Sexual Negotiation among Married Women in Bangladesh

Evaluating the Performance of Machine Learning Algorithm for Classification of Safer Sexual Negotiation among Married Women in Bangladesh

Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review.

The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.

Usage of Weka Software Based On Machine Learning Algorithms for Prediction of Liver Fibrosis/Cirrhosis

The liver, a life-sustaining organ, plays a substantial role in many body functions. Liver diseases have become an important world health problem in terms of prevalence, incidences, and mortalities. Liver fibrosis/cirrhosis is great of importance, because if not treated in time liver cancer could be occurred and spread to other parts of the body. For this reason, early diagnosis of liver fibrosis/cirrhosis gives significance. Accordingly, this study investigated the performances of different machine learning algorithms for prediction of liver fibrosis/cirrhosis based on demographic and blood values. In this context, random forest, k nearest neighbour, C4.5 decision tree, K-star, random tree and reduced error pruning tree algorithms were used. Two distinct approaches were employed to evaluate the performances of machine learning algorithms. In the first approach, the entire features of dataset were utilized, while in the second approach, only the features selected through principal component analysis were used. Each approach was rigorously assessed using both 10-fold cross-validation and data splitting (70% train and 30% test) techniques. By conducting separate evaluations for each approach, a comprehensive understanding of the effectiveness of utilizing all features versus extracted features based principal component analysis was attained, providing valuable insights into the impact of feature dimensionality reduction on model performance. In this study, all analyses were implemented on WEKA data mining tool. In the first approach, the classification accuracies of random forest algorithm were 89.72% and 90.75% with the application of data splitting (70%-30%) and cross-validation techniques, respectively. In the second approach, where feature reduction is performed using principal component analysis technique, the accuracy values obtained from data splitting and cross-validation techniques of random forest algorithm were 88.61% and 88.83%, respectively. The obtained results revealed out that random forest algorithm outperformed for both approaches. Besides, the application of principal component analysis technique negatively affected the classification performance of used machine learning algorithms. It is thought that the proposed model will guide specialist physicians in making appropriate treatment decisions for patients with liver fibrosis/cirrhosis, potentially leading to death in its advanced stages.

Exploring Machine Learning Algorithms for Enhanced Diagnosis of Breast Cancer : A Comparative Study

Breast cancer remains a significant global health concern, necessitating accurate and efficient diagnostic methods for timely intervention and treatment. This study investigates the efficacy of various machine learning algorithms in diagnosing breast cancer based on clinical data. Leveraging a comprehensive dataset comprising demographic information, medical history, and diagnostic features, we employ supervised learning techniques to train and evaluate multiple classifiers. Through a comparative analysis, we assess the performance of popular machine learning algorithms including logistic regression, support vector machines, decision trees, random forests, and neural networks. Evaluation metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC) are utilized to quantify the diagnostic capabilities of each model. Our results demonstrate promising performance across the evaluated algorithms, with some exhibiting superior accuracy and predictive power compared to others. Furthermore, we explore feature importance to gain insights into the characteristics influencing the classification process. This research contributes to the growing body of literature on utilizing machine learning for medical diagnosis, offering valuable insights for developing robust and accurate tools for detecting breast cancer.

Remote sensing estimation of δ15NPN in the Zhanjiang Bay using Sentinel-3 OLCI data based on machine learning algorithm

Remote sensing estimation of δ15NPN in the Zhanjiang Bay using Sentinel-3 OLCI data based on machine learning algorithm

Exploration of Eye Fatigue Detection Features and Algorithm Based on Eye-Tracking Signal

Eye fatigue has a fatiguing effect on the eye muscles, and eye movement performance is a macroscopic response to the eye fatigue state. To detect and prevent the risk of eye fatigue in advance, this study designed an eye fatigue detection experiment, collected experimental data samples, and constructed experimental data sets. In this study, eye-tracking feature extraction was completed, and the significance difference of eye-tracking features under different fatigue states was discussed by two-way repeated-measures ANOVA (Analysis of Variance). The experimental results demonstrate the feasibility of eye fatigue detection from eye-tracking signals. In addition, this study considers the effects of different feature extraction methods on eye fatigue detection accuracy. This study examines the performance of machine learning algorithms based on manual feature calculation (SVM, DT, RM, ET) and deep learning algorithms based on automatic feature extraction (CNN, auto-encoder, transformer) in eye fatigue detection. Based on the combination of the methods, this study proposes the feature union auto-encoder algorithm, and the accuracy of the algorithm for eye fatigue detection on the experimental dataset is improved from 82.4% to 87.9%.

Exploiting building information modeling and machine learning for optimizing rooftop photovoltaic systems

The primary objective of this study is to develop a strategy to maximize the potential of Building Applied Photovoltaics (BAPV) by providing researchers and experts in the field with the appropriate tools. By utilizing operational data from an operational roof-mounted BAPV system and incorporating Building Information Modeling (BIM) to improve its design and smooth integration into built settings, the study offers novel insights.A three-phase research methodology is applied, encompassing data collection and performance assessment, BIM modeling, and machine learning algorithms for energy forecasting. The case study involves a 160 kWp photovoltaic system in Abruzzo, Italy, over a three-year period. The research employs different performance metrics recommended by IEC 61724 to compare experimental and theoretical data. BIM simulations are exploited as they are crucial for identifying the correct design process. Furthermore, four cutting-edge machine learning algorithms are used to forecast daily energy production. The random forest is identified as the best model for forecasting and the effect of tuning of hyperparameters on the efficiency of all models is also reported.The research adds to the larger conversation on sustainable energy management and solutions by providing researchers involved in the design and improvement of BAPV systems with a solid foundation for future developments.

Assessment of soil salinity using explainable machine learning methods and Landsat 8 images

The aim of this study is to comparatively analyze the performance of machine learning (ML) algorithms for modeling soil salinity using field-based electrical conductivity (EC) data and Landsat-8 OLI satellite images with derived environmental covariates. We also aim to interpret and explain the ML models with and without over-sampling methods using Shapley (SHAP) values, an explainable ML approach that has not yet been utilized for soil salinity estimation tasks as an ML problem. We investigate two case study areas from western and southeastern Lake Urmia Playas (LUP) in the Northwest of Iran. Our study uses 26 environmental covariates, two ML models, namely extreme gradient boosting (XGBoost) and random forest (RF), and two over-sampling methods: synthetic minority over-sampling technique (SMOTE) and random over-sampling (ROS). Results indicate that XGBoost performs better compared to RF in terms of both R2 and RMSE. Additionally, the visual interpretation of soil salinity maps demonstrated the superiority of XGBoost. SMOTE produced superior results than ROS and no over-sampling test cases. Finally, SHAP analysis illustrated that vegetation indices made a greater contribution to the soil salinity prediction in the West LUP, while visible bands contributed more in the Southeast LUP Region.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study

BackgroundHospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.AimThis study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI.MethodsThis retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011–2012 and 2019–2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance.ResultsPatients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results.ConclusionThis study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Automated segmentation for early detection of uveal melanoma

ObjectiveUveal melanoma is the most common intraocular malignancy in adults. Current screening and triaging methods for melanocytic choroidal tumours face inherent limitations, particularly in regions with limited access to specialized ocular oncologists. This study explores the potential of machine learning to automate tumour segmentation. We develop and evaluate a machine-learning model for lesion segmentation using ultra-wide-field fundus photography. MethodA retrospective chart review was conducted of patients diagnosed with uveal melanoma, choroidal nevi, or congenital hypertrophy of the retinal pigmented epithelium at a tertiary academic medical centre. Included patients had a single ultra-wide-field fundus photograph (Optos PLC, Dunfermline, Fife, Scotland) of adequate quality to visualize the lesion of interest, as confirmed by a single ocular oncologist. These images were used to develop and test a machine-learning algorithm for lesion segmentation. ResultsA total of 396 images were used to develop a machine-learning algorithm for lesion segmentation. Ninety additional images were used in the testing data set along with images of 30 healthy control individuals. Of the images with successfully detected lesions, the machine-learning segmentation yielded Dice coefficients of 0.86, 0.81, and 0.85 for uveal melanoma, choroidal nevi, and congenital hypertrophy of the retinal pigmented epithelium, respectively. Sensitivities for any lesion detection per image were 1.00, 0.90, and 0.87, respectively. For images without lesions, specificity was 0.93. ConclusionOur study demonstrates a novel machine-learning algorithm's performance, suggesting its potential clinical utility as a widely accessible method of screening choroidal tumours. Additional evaluation methods are necessary to further enhance the model's lesion classification and diagnostic accuracy.

Precision healthcare analytics: Integrating ML for automated image interpretation, disease detection, and prognosis prediction

This review paper provides an overview of precision healthcare analytics, focusing on the integration of machine learning (ML) techniques for automating image interpretation, disease detection, and prognosis prediction across various medical imaging modalities, including X-rays, MRIs, and CT scans. Drawing upon existing literature and empirical evidence, we assess the impact of ML-driven automated image interpretation on diagnostic accuracy, highlighting its superiority over traditional methods. Additionally, we examine the effectiveness of ML algorithms in disease detection, emphasizing their potential for early intervention and improved patient outcomes. Furthermore, we explore the prognostic capabilities of ML-based models in forecasting disease progression and guiding treatment strategies. Through a comprehensive synthesis of research findings, we identify key factors influencing the performance of ML algorithms in healthcare applications and discuss strategies for addressing challenges related to data quality, interpretability, and scalability. By critically evaluating current trends and advancements in precision healthcare analytics, this review aims to provide insights into the potential benefits and limitations of ML integration in medical practice, contributing to the ongoing discourse on enhancing patient care and healthcare delivery.

Comparison and analysis of prediction accuracy between traditional machine learning algorithms and XGBoost algorithm in music emotion classification

Music emotion classification refers to determining the emotional types of music, such as happiness, sadness, anger, and passion, based on aspects like rhythm, melody, and tone. The development of artificial intelligence can be applied to music recommendation by employing machine learning algorithms to ascertain emotional attributes of music, enabling precise music suggestions for users to enhance their musical experience. This paper compares and analyzes the classification performance of traditional Random Forest machine learning algorithms and the XGBoost model on a Turkish music emotion dataset. We selected a comprehensive dataset from Kaggle, containing a vast array of music samples and their corresponding emotional labels, making it an extensive music emotion classification dataset. From the experimental results, it's evident that the traditional Random Forest machine learning model outperforms the XGBoost model in terms of accuracy, precision, and recall. The accuracy of the traditional Random Forest machine learning model stands at 80.8%, whereas the XGBoost model's accuracy is 75%. The recall rate for the traditional Random Forest machine learning model is 80.8%, while for XGBoost, it's 77.2%. The F1 score for the traditional Random Forest machine learning model is 80.5%, whereas for XGBoost, it's 75.3%. These parameters indicate that the traditional Random Forest machine learning model exhibits superior predictive performance in music emotion classification. However, the XGBoost model possesses its own advantages, such as faster learning and prediction speeds, high accuracy, and strong generalization capabilities. In summary, the traditional Random Forest machine learning model demonstrates better robustness and interpretability, effectively handling samples with noise and missing data, thus finding widespread practical application. On the other hand, the XGBoost model excels in rapid training and prediction, coupled with higher accuracy and versatility, making it advantageous in dealing with large datasets. The research outcomes of this paper hold significant importance for the study and application of music emotion classification. The experimental results presented herein offer valuable insights for researchers and practitioners, aiding them in selecting appropriate machine learning models, optimizing, and adjusting them to achieve the best classification results.

Evaluation of multiple machine learning models for ASR expansion of concrete

Machine learning algorithms (MLA) show promise in predicting alkali-silica reaction (ASR) expansion. Nevertheless, the predictive performance of different machine learning algorithms (MLAs) varies across various engineering scenarios and datasets. Given that, this study was devoted to constructing an appropriate predictive model for ASR expansion of concrete. Multiple MLAs including evolutionary-based, support vector-based, assembly-based, and neural network-based algorithms, were employed to establish predictive models. An optimization technique, the particle swarm optimization algorithm, was also utilized and integrated with the aforementioned MLA models to effectively search for optimal hyperparameters and topological structures. The predictive performance was evaluated using statistical methods, which showed that the extreme gradient boosting (XGBoost) model had the best fitting goodness among the standard algorithms, with an R2 value of 0.9695. Moreover, the Particle Swarm Optimization (PSO) model significantly improved the predictive performance of all Machine Learning Algorithm (MLA) models. The error distribution analysis was then performed. There are fewer large errors in the predicted values of the PSO-XGBoost model. Monte Carlo simulation and probability distribution functions were used to thoroughly analyze the stability of each model after multiple independent runs. The results show that all models have excellent stability. The PSO-XGBoost model performed the best and was subsequently employed to conduct feature analysis to determine the significance of each input parameter and capture the underlying evidence of ASR expansion.

An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA

Network intrusion detection is an essential component of contemporary cybersecurity strategies, and the development of efficient techniques to accurately identify malicious activities has become a priority. This study investigates the performance of various conventional machine learning algorithms, including decision trees, naive Bayes, naive Bayes trees, random forest, random trees, MLP, and SVM, in detecting network intrusions using binary and multi-classification approaches. Furthermore, the study proposes a deep learning method, CNN-LSTM-SA, which consistently outperforms conventional machine learning techniques in terms of precision, recall, F1 score, and overall accuracy for network intrusion detection. Specifically, the proposed method combines CNN and LSTM with SA in machine learning theory to extract more optimized, strongly correlated features. The proposed method is evaluated using the benchmark NSL-KDD database. The results indicate that the CNN-LSTM-SA method holds great potential in enhancing the efficacy of network intrusion detection systems.

CHRONIC KIDNEY DISEASE PREDICTION USING MACHINE LEARNING

The term ―chronic kidney disease‖ means lasting damage to the kidneys that can get worse over time. If the damage is very bad, your kidneys may stop working. This is called kidney failure, or end-stage Kidney disease (ESRD). Kidney disease patients have the potential to get into the chronic phase and chronic kidney disease (CKD) is a decrease in kidney function gradually. So, doctors can diagnose kidney disease patients. So, predicting whether patients with Kidney disease have entered a phase of chronic kidney disease or not by showing the best accuracy result of comparing supervised classification machine learning algorithms. The aim is to investigate machine learning- based techniques for CKD forecasting by predicting results in the best accuracy. The analysis of the dataset by supervised machine learning technique (SMLT) to capture several information like, variable identification, univariate analysis, bi-variate and multi-variate analysis, missing value treatments and analyze the data validation, data cleaning/preparation, and data visualization will be done on the entire given dataset. Additionally, to compare and discuss the performance of various machine learning algorithms from the given hospital dataset with an evaluation classification report, identify the confusion matrix and to categorizing data from priority and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared with the best accuracy with precision, Recall and F1 Score. Key Words: Chronic kidney, Damage, Univariate, bi-variate, Multi-variate.

Thermodynamic Stability Prediction of Triple Transition‐Metal (Ti−Mo−V)3C2${\rm (Ti-Mo-V)}_3{\rm C}_2$ MXenes via Cluster Correlation‐Based Machine Learning

AbstractThe representation of atomic configurations through cluster correlations, along with the cluster expansion approach, has long been used to predict formation energies and determine the thermodynamic stability of alloys. In this work, a comparison is conducted between the traditional cluster expansion method based on density functional theory and other potential machine learning models, including decision tree‐based ensembles and multi‐layer perceptron regression, to explore the alloying behavior of different elements in multi‐component alloys. Specifically, these models are applied to investigate the thermodynamic stability of triple transition‐metal MXenes, a multi‐component alloy in the largest family of 2D materials that are gaining attention for several outstanding properties. The findings reveal the triple transition‐metal ground‐state configurations in this system and demonstrate how the configuration of transition metal atoms (Ti, Mo, and V atoms) influences the formation energy of this alloy. Moreover, the performance of machine learning algorithms in predicting formation energies and identifying ground‐state structures is thoroughly discussed from various aspects.

Predicting maritime accident risk using Automated Machine Learning

Machine learning (ML), particularly, Automated machine learning (AutoML) offers a range of possibilities for analysing large volumes of historical maritime accidents data with advanced algorithms for integrating predictive analytics in operational and policy decision-making for improving maritime safety. This study explores historical data of maritime accidents in Norwegian waters over 40 years. The data has been utilised for analysing five major maritime accident categories: grounding, contact damage, fire or explosion, collision, and heavy weather damage. A total of 29 classification ML algorithms were trained, and the Light Gradient Boosted Trees Classifier was found to be the best-performing with the highest predictive accuracy. The three most impactful factors for accident risk are the category of navigation waters, phase of operation, and gross tonnage of the vessel. Based on the feature effect results, vessels sailing in narrow coastal waters, in the along-the-way operational phase, and fishing vessels are highly vulnerable to grounding relative to other types of accidents. The results can be used as input for the entire procedure of risk analysis, from hazard identification to quantification of accident consequences, and the best-performing ML algorithm can be utilized in developing a decision support system for real-time maritime accident risk assessment.

Machine learning for predicting colon cancer recurrence

Colorectal cancer (CRC) is a global public health concern, ranking among the most commonly diagnosed malignancies worldwide. Despite advancements in treatment modalities, the specter of CRC recurrence remains a significant challenge, demanding innovative solutions for early detection and intervention. The integration of machine learning into oncology offers a promising avenue to address this issue, providing data-driven insights and personalized care. This retrospective study analyzed data from 396 patients who underwent surgical procedures for colon cancer (CC) between 2010 and 2021. Machine learning algorithms were employed to predict CC recurrence, with a focus on demographic, clinicopathological, and laboratory characteristics. A range of evaluation metrics, including AUC (Area Under the Receiver Operating Characteristic), accuracy, recall, precision, and F1 scores, assessed the performance of machine learning algorithms. Significant risk factors for CC recurrence were identified, including sex, carcinoembryonic antigen (CEA) levels, tumor location, depth, lymphatic and venous invasion, and lymph node involvement. The CatBoost Classifier demonstrated exceptional performance, achieving an AUC of 0.92 and an accuracy of 88% on the test dataset. Feature importance analysis highlighted the significance of CEA levels, albumin levels, N stage, weight, platelet count, height, neutrophil count, lymphocyte count, and gender in determining recurrence risk. The integration of machine learning into healthcare, exemplified by this study's findings, offers a pathway to personalized patient risk stratification and enhanced clinical decision-making. Early identification of individuals at risk of CC recurrence holds the potential for more effective therapeutic interventions and improved patient outcomes. Machine learning has the potential to revolutionize our approach to CC recurrence prediction, emphasizing the synergy between medical expertise and cutting-edge technology in the fight against cancer. This study represents a vital step toward precision medicine in CC management, showcasing the transformative power of data-driven insights in oncology.

SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts.

Fall prediction in a quiet standing balance test via machine learning: Is it possible?

The elderly population is growing rapidly in the world and falls are becoming a big problem for society. Currently, clinical assessments of gait and posture include functional evaluations, objective, and subjective scales. They are considered the gold standard to indicate optimal mobility and performance individually, but their sensitivity and specificity are not good enough to predict who is at higher risk of falling. An innovative approach for fall prediction is the machine learning. Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. Thus, to assess the performance of machine learning algorithms in classify participants by age, number of falls and falls frequency based on features extracted from a public database of stabilometric assessments. 163 participants (116 women and 47 men) between 18 and 85 years old, 44.0 to 75.9 kg mass, 140.0 to 189.8 cm tall, and 17.2 to 31.9 kg/m2 body mass index. Six different machine learning algorithms were tested for this classification, which included Logistic Regression, Linear Discriminant Analysis, K Nearest-neighbours, Decision Tree Classifier, Gaussian Naive Bayes and C-Support Vector Classification. The machine learning algorithms were applied in this database which has sociocultural, demographic, and health status information about participants. All algorithm models were able to classify the participants into young or old, but our main goal was not achieved, no model identified participants at high risk of falling. Our conclusion corroborates other works in the biomechanics field, arguing the static posturography, probably due to the low daily living activities specificity, does not have the desired effects in predicting the risk of falling. Further studies should focus on dynamic posturography to assess the risk of falls.