ML Model Research Articles

A novel approach for estimating blood flow dynamics factors of eccentric stenotic arteries based on ML

Reliable and rapid estimation of blood flow dynamics factors in eccentric stenotic arteries could significantly improve clinical treatments. Numerical simulation methods such as FSI and CFD are widely used to investigate blood flow conditions. However, both FSI and CFD are computationally expensive and not suitable for large-scale research. This work proposes an effective approach for estimating the blood flow dynamics factors of eccentric stenotic arteries based on ML. The estimation approach includes three steps: (a) blood flow conditions of idealized eccentric stenotic arteries modeled with different geometric parameters were simulated by CFD and FSI, and the error between CFD and FSI results were evaluated, (b) datasets of nonlinear relationships between discretized geometric parameters and blood flow dynamics factors were created using CFD, and (c) blood flow dynamics factors of eccentric stenotic arteries were estimated by carefully designed ML model. The accuracy validation was conducted by different representative cases, which have different combinations of geometric parameters including stenosis severity, eccentricity, and stenosis plaque length. The ML model can output the blood flow dynamics factors including peak wall shear stress and peak systole velocity accurately within 1 s, which shows proposed approach not only achieves accurate estimation of blood flow dynamics factors but also bypasses the expensive computational cost.

The application of machine learning techniques for smart irrigation systems: A systematic literature review

Smart Irrigation System is a complex concept used to control, monitor and automate the irrigation of yields by integrating artificial intelligence techniques such as Machine Learning strategies. SIS has endorsed various machine learning models. However, there has been no attempt to analyze the empirical evidence on ML models in a systematic way. Moreover, ML based SIS often face problems and raise questions that must be resolved. This article presents a systematic literature review of ML based SIS; an overview of the literature on ML is designed, revealing a premier and unbiased survey of the existing empirical research. 55 selected studies published from 2017 to 2023 and nine broadly used ML models were identified. Furthermore, four analysis aspects namely type of ML technique, estimation accuracy, model comparison, and estimation context have been outlined. The findings of this review prove the performance capability of the ML techniques endorsed within SIS. The ML techniques outperform other conventional approaches. However, the application of ML models in SIS is still limited and more effort is needed to obtain well-formed and generalizable results. To this end, and based on the outcomes obtained in this work, future guidelines have been provided to practitioners and researchers to grasp the major contributions and challenges in the state-of-the-art research.

Development and external validation of a machine learning model for prediction of survival in extremity leiomyosarcoma

PurposeMachine learning (ML) models have been used to predict cancer survival in several sarcoma subtypes. However, none have investigated extremity leiomyosarcoma (LMS). ML is a powerful tool that has the potential to better prognosticate extremity LMS. MethodsThe Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologic extremity LMS (n = 634). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of extremity LMS patients (n = 46). ResultsAll ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.75–0.76 at the 5-year time point. The Random Forest (RF) model was the best performing model and used for external validation. This model also performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.90 and 0.87, respectively. The RF model was well calibrated on external validation. This model has been made publicly available at https://rachar.shinyapps.io/lms_app/ ConclusionsML models had excellent performance for survival prediction of extremity LMS. Future studies incorporating a larger institutional cohort may be needed to further validate the ML model for LMS prognostication.

GMM‐LIME explainable machine learning model for interpreting sensor‐based human gait

AbstractMachine learning (ML) has been used in human gait data for appropriate assistive device prediction. However, their uptake in the medical setup still remains low due to their black box nature which restricts clinicians from understanding how they operate. This has led to the exploration of explainable ML. Studies have recommended local interpretable model‐agnostic explanation (LIME) because it builds sparse linear models around an individual prediction in its local vicinity hence fast and also because it could be used on any ML model. LIME is however, is not always stable. The research aimed to enhance LIME to attain stability by avoid the sampling step through combining Gaussian mixture model (GMM) sampling. To test performance of the GMM‐LIME, supervised ML were recommended because study revealed that their accuracy was above 90% when used on human gait. Neural networks were adopted for GaitRec dataset and Random Forest (RF) was adopted and applied on HugaDB datasets. Maximum accuracies attained were multilayer perceptron (95%) and RF (99%). Graphical results on stability and Jaccard similarity scores were presented for both original LIME and GMM‐LIME. Unlike original LIME, GMM‐LIME produced not only more accurate and reliable but also consistently stable explanations.

Dynamic selection of machine learning models for time-series data

Adapting to changes in time-series data is a major challenge in machine learning. This problem is particularly acute in the case of limited computing power (e.g., edge devices) that does not enable the independent training of new models. While multiple studies attempt to adequately train a machine learning model on rapidly shifting data (i.e., concept drift), the challenge of dynamically selecting the most effective machine learning model for future time steps has been largely overlooked. In this study, we propose Adaptive machine learning for Dynamic ENvironments (ADEN ), a method for analyzing future trends in time-series data and selecting the most suitable ML model. Our approach models multiple aspects of the analyzed data and analyzes the behavior of multiple machine learning models on earlier time steps. By training ADEN on multiple datasets, we can deploy a zero-shot model that does not require additional training when applied to new datasets. Our evaluation, conducted on 46 time-series classification datasets, shows that ADEN not only outperforms all baselines in terms of average performance but is also capable of avoiding sudden drops in performance that characterize all other evaluated algorithms.

Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression

BackgroundSmall airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline.MethodsPRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model.ResultsMultivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (β of 0.106, p < 0.001) and VfSAD (β of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69.ConclusionsWe demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Uncertainty quantification of the flexural strengthening of RC beams with UHPC using improved metaheuristic algorithm

This study presents an artificial intelligence-based model to predict the flexural strengthening of RC beam/slab strengthened with UHPC (RC-UHPC) and perform uncertainty quantification of such values. Improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) was a model developed on artificial neural network (ANN) optimized by improved-eliminate-particle-swamp optimization (IEPSO). Experimental datasets were used to establish the hybrid ML model. The efficiency of IEPANN was compared against support vector regression (SVR) and multiple linear regression (MLR). The IEPANN demonstrates outstanding performance over MLR and SVR. The flexural strengthening of RC-UHPC depends on materials and geometrical configurations, which have stochastic characteristics. Convergence studies, sensitivity, and probabilistic analyses were conducted on each random parameter. Simulation results indicated that strengthening configuration is the most sensitive to flexural strengthening. Systematic assessment of uncertainties in flexural strengthening may lead to a higher confidence level in model outcomes, and RC-UHPC is examined and designed. It enhances construction reliability.

MedLingua: A conceptual framework for a multilingual medical conversational agent

This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.

Supervised and unsupervised learning of (1+1) -dimensional even-offspring branching annihilating random walks

Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1) -d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1) -d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent z and spatial correlation length correlation exponent ν⊥ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1) -d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.

Shear bearing capacity prediction of STRC shear walls using data‐augmented fusion model

AbstractMachine learning (ML) accurately predicts the shear bearing capacity of steel tube‐reinforced concrete (STRC) shear walls, aiding optimization design. However, creating a database for STRC shear walls experimentally is time‐consuming and costly. The aim of this study is to propose a method for high‐accuracy prediction of STRC shear wall shear bearing capacity using a small‐sample dataset. This study employs generative adversarial network (GAN) data augmentation techniques to address the issues of insufficient ML model training and low prediction accuracy in small‐sample databases. Based on the stacking framework, a fusion model (Stacking‐XRL) combining extreme gradient boosting (XGBoost), random forest (RF), and least absolute shrinkage and selection operator (LASSO) is established to predict the shear bearing capacity of STRC shear walls. Results show that after augmenting the training set with GAN, the prediction performance of K‐nearest neighbors (KNN), backpropagation neural network (BPNN), RF, light gradient boosting machine (LightGBM), XGBoost, and stacking‐XRL models significantly improve, with average increases of 10% in R2 and average decreases of 30% and 25% in MAE and RMSE, respectively. The proposed stacking‐XRL fusion model outperforms tested models, existing formulas, and Abaqus numerical simulations for the shear bearing capacity of STRC shear walls. Model interpretation reveals that the shear span ratios as the most important factors in predicting shear bearing capacity, followed by axial force ratio and whole section configuration steel tubular index.

Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data

BackgroundMachine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. MethodsIn this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization); (2) it safeguards patients’ privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process; and (3) it facilitates the ML model training without relying on a centralized party/server. FindingsWe demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private datasets from individual parties without collaboration and those trained with the previous privacy-preserving collaborative training framework under the same privacy guarantee by up to 70% and 18.2% respectively. InterpretationWe demonstrate that the ML models trained with DeCaPH framework have an improved utility-privacy trade-off, showing DeCaPH enables the models to have good performance while preserving the privacy of the training data points. In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability. FundingThis work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2020-06189 and DGECR-2020-00294), Canadian Institute for Advanced Research (CIFAR) AI Catalyst Grants, CIFAR AI Chair programs, Temerty Professor of AI Research and Education in Medicine, University of Toronto, Amazon, Apple, DARPA through the GARD project, Intel, Meta, the Ontario Early Researcher Award, and the Sloan Foundation. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.

A Comparative Study of Machine Learning and Algorithmic Approaches to Automatically Identify the Yield Point in Normal and Aneurysmal Human Aortic Tissues.

The stress-strain curve of biological soft tissues helps characterize their mechanical behavior. The yield point on this curve is when a specimen breaches its elastic range due to irreversible microstructural damage. The yield point is easily found using the offset yield method in traditional engineering materials. However, correctly identifying the yield point in soft tissues can be subjective due to its nonlinear material behavior. The typical method for yield point identification is visual inspection, which is investigator-dependent and does not lend itself to automation of the analysis pipeline. An automated algorithm to identify the yield point objectively assesses soft tissues' biomechanical properties. This study aimed to analyze data from uniaxial extension testing on biological soft tissue specimens and create a machine learning (ML) model to determine a tissue sample's yield point. We present a trained machine learning model from 279 uniaxial extension curves from testing aneurysmal/nonaneurysmal and longitudinal/circumferential oriented tissue specimens that multiple experts labeled through an adjudication process. The ML model showed a median error of 5% in its estimated yield stress compared to the expert picks. The study found that an ML model could accurately identify the yield point (as defined) in various aortic tissues. Future studies will be performed to validate this approach by visually inspecting when damage occurs and adjusting the model using the ML-based approach.

Fatal Crash Occurrence Prediction and Pattern Evaluation by Applying Machine Learning Techniques

Background Highway safety remains a significant issue, with road crashes being a leading cause of fatalities and injuries. While several studies have been conducted on crash severity, few have analyzed and predicted specific types of crashes, such as fatal crashes. Identifying the key factors associated with fatal crashes and predicting their occurrence can help develop effective preventative measures. Objective This study intended to develop cluster analysis and ML-based models using crash data to extract the prominent factors behind fatal crash occurrences and analyze the inherent pattern of variables contributing to fatal crashes. Methods Several branches and categories of supervised ML models have been implemented for fatality prediction and their results have been compared. SHAP analysis was conducted using the ML model to explore the contributing factors of fatal crashes. Additionally, the underlying hidden patterns of fatal crashes have been evaluated using K-means clustering, and specific fatal crash scenarios have been extracted. Results The deep neural networks model achieved 85% accuracy in predicting fatal crashes in Kansas. Factors, such as speed limits, nighttime, darker road conditions, two-lane highways, highway interchange areas, motorcycle and tractor-trailer involvement, and head-on collisions were found to be influential. Moreover, the clusters were able to discern certain scenarios of fatal crashes. Conclusion The study can provide a clear image of the important factors related to fatal crashes, which can be utilized to create new safety protocols and countermeasures to reduce fatal crashes. The results from cluster analysis can facilitate transportation professionals with representative scenarios, which will benefit in identifying potential fatal crash conditions.

Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models

BackgroundMachine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE). ObjectiveThe purpose of this study was to test whether integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAEs; procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes). MethodsWe hypothesized certain features—(1) lead angulation, (2) coil percentage inside the superior vena cava (SVC), and (3) number of overlapping leads in the SVC—detected from a pre-TLE plain anteroposterior chest radiograph (CXR) would improve prediction of MAE and long procedural times. A deep-learning convolutional neural network was developed to automatically detect these CXR features. ResultsA total of 1050 cases were included, with 24 MAEs (2.3%) . The neural network was able to detect (1) heart border with 100% accuracy; (2) coils with 98% accuracy; and (3) acute angle in the right ventricle and SVC with 91% and 70% accuracy, respectively. The following features significantly improved MAE prediction: (1) ≥50% coil within the SVC; (2) ≥2 overlapping leads in the SVC; and (3) acute lead angulation. Balanced accuracy (0.74–0.87), sensitivity (68%–83%), specificity (72%–91%), and area under the curve (AUC) (0.767–0.962) all improved with imaging biomarkers. Prediction of lengthy procedures also improved: balanced accuracy (0.76–0.86), sensitivity (75%–85%), specificity (63%–87%), and AUC (0.684–0.913). ConclusionRisk prediction tools integrating imaging biomarkers significantly increases the ability of ML models to predict risk of MAE and long procedural time related to TLE.

The Potential Analytical Impact of Significant Sectoral Creative Economy on Thailand’s Economy: A Case Study of the IRS-CGE Model vs. the CRS-CGE Model for Both the National and Provincial Economies

The United Nations has promoted and supported the UNCTAD Creative Economy Programme since 2004 to help countries around the world understand how to promote economic development through creativity in industries. This research article aims to determine whether the creative economy will be the major engine to accelerate Thailand’s economic development in the coming decade or not, and what the major creative economy sectors are that must be prioritized or initiated and focused on. The data implemented in this research cover 2011–2018, which consist of creative economy sector income, the IO table, and the SAM table. The methodology utilized in this research was the ML model, the GREY model for predicting the growth rate of income from the major creative economy sectors contribute to Thailand’s economy between 2019–2025, and the CGE model. The study’s empirical findings show that the significant sectoral creative economy consists of fashion, advertising, Thai food, and cultural tourism, which need to be given more stimulus. Furthermore, the economies of Chiang Mai, and Thailand as a whole, would eventually be high-income economies if creative economy sectors were to be promoted and continuously supported by efficient policies. the economic growth of Thailand and Chiang Mai would eventually become high income whenever these economies allow creative economy sectors to be promoted or supported by efficient policies continuously.

The five-factor model analysed by machine learning classification techniques

ABSTRACT This study compares the classification performance of traditional factor models and ML to explore the assumption of linearity in risk premiums. We apply ML techniques to the variables used in the Fama – French five-factor (FF5) model to classify the top 30% and bottom 30% of stocks based on whether their prices are expected to increase (winners) or decrease (losers), respectively, over a specified time period. The analysis showed that the ML model’s classification Accuracy outperforms the FF5 classification, which suggests that the higher returns obtained using the FF5 model can be attributed to its effectiveness in classifying stocks with exceptionally high or low returns, rather than the comprehensive use of its variables for overall classification purposes.

Accelerated modeling and design of a mixed refrigerant cryogenic process using a data-driven approach

Cryogenic processes with mixed refrigerants are prevalent in energy-intensive chemical industries, enhancing energy efficiency while reducing costs and unit size. However, the curse of dimensionality and process design constraints pose significant hurdles for effective screening and optimization. To tackle this, we developed a neural network model for natural gas liquefaction prediction. Trained on an extensive Aspen HYSYS database, our ML model accurately simulates LNG processes, with an impressive R2 test value of 99.63, operating almost ten million times faster than HYSYS. It effectively addresses vital process design constraints, including liquid slugging and temperature cross, crucial for optimization. By integrating the ML model with genetic and Nelder–Mead algorithms, we achieve an 8.9% reduction in total exergy, outperforming Aspen HYSYS within the same time frame. Our study underscores ML’s significance in modeling energy-intensive chemical processes, providing insights into the exergy profile and enabling feature importance analysis.

Fairness- and Uncertainty-Aware Data Generation for Data-Driven Design Based on Active Learning

Abstract The design dataset is the backbone of data-driven design. Ideally, the dataset should be fairly distributed in both shape and property spaces to efficiently explore the underlying relationship. However, the classical experimental design focuses on shape diversity and thus yields biased exploration in the property space. Recently developed methods either conduct subset selection from a large dataset or employ assumptions with severe limitations. In this paper, fairness- and uncertainty-aware data generation (FairGen) is proposed to actively detect and generate missing properties starting from a small dataset. At each iteration, its coverage module computes the data coverage to guide the selection of the target properties. The uncertainty module ensures that the generative model can make certain and thus accurate shape predictions. Integrating the two modules, Bayesian optimization determines the target properties, which are thereafter fed into the generative model to predict the associated shapes. The new designs, whose properties are analyzed by simulation, are added to the design dataset. This constructs an active learning mechanism that iteratively samples new data to improve data representativeness and machine learning model performance. An S-slot design dataset case study was implemented to demonstrate the efficiency of FairGen in auxetic structural design. Compared with grid and randomized sampling, FairGen increased the coverage score at twice the speed and significantly expanded the sampled region in the property space. As a result, the generative models trained with FairGen-generated datasets showed consistent and significant reductions in mean absolute errors.

Identifying the Learning Style of Students Using Machine Learning Techniques: An Approach of Felder Silverman Learning Style Model (FSLSM)

Identification of the learning style of the students in the teaching and learning environment plays a significant importance in improving both teaching and learning perspectives. The intension of the research was to investigate about applying the Machine Learning Techniques for identification of the Learning style of the students in online learning environment based on the Felder Silverman Learning Style (FSLSM) identification model. The significance of this experiment is that the proposed methodology considers the combination of access frequency (f) of course materials and total time (T) students spent on each course activity. For data collection process, it was designed reusable plugin for the Moodle for time tracking. Real-time dataset was prepared using three course modules designed according to the features of FSLSM model and the features for analyzing the students according to the FSLSM, it was selected seven criteria, and the features were validated using Pearson Correlation Coefficient method. These course modules were enrolled 150 students per each module. Once the data set was prepared, the data set was preprocessed and applied Five Supervised Classification Machine learning algorithms as Decision Tree, Logistic Regression, Random Forest, Support Vector Machine and K-Nearest Neighbors algorithm. The models were evaluated using Accuracy, Precision, Recall and F1 values. Over the five algorithms the Decision Tree classifier algorithm performed with best average accuracy with 93.5% for Input, 86% for Perception, 89.5 for Processing and 94% for Understanding dimension. The models were validated using the K-fold Cross validation and Standard Deviation values. Mean Squared Error, Bias and Variance values were considered the evaluation of underfitting or overfitting context of the model. To parameter optimization, the Grid Search Methodology was applied to find the best combination of criterion for the model. Finally, an application was developed for Identifying the Learning Style of the Students using the designed Machine learning model. The Consistency of the ML Model based on the Decision Tree classifier algorithm were evaluated using the results generated through developed application and the results suggested that consistency for taught machine learning algorithms is often between 85% to 95%, which is an acceptable range. The results generated by the application for identification of the learning style suggested the combination of learning style for particular students sample as Global-Mild, Visual- Strong, Sensing- Moderate and Reflective-Strong. Identification of these combination of learning style assist for teachers by giving an insight which components of the learning contents should be improved in course designing process.

A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.

Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities. A total of 278 patients with WQCT from January 2010 to March 2022 were enrolled. The electrophysiological study confirmed SVT in 154 patients and VT in 65 ones. Two hundred nineteen WQCT 12-lead ECGs were randomly divided into development cohort (n = 165) and testing cohort (n = 54) data sets. The development cohort was split into a training group (n = 115) and an internal validation group (n = 50). Forty ECG features extracted from the 219 WQCT ECGs are fed into 9 iteratively trained ML algorithms. This novel ML algorithm was also compared with four published algorithms. In the development cohort, the Gradient Boosting Machine (GBM) model displayed the maximum area under curve (AUC) (0.91, 95% confidence interval (CI) 0.81-1.00). In the testing cohort, the GBM model had a higher AUC of 0.97 compared to 4 validated ECG algorithms, namely, Brugada (0.68), avR (0.62), RWPTII (0.72), and LLA algorithms (0.70). Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the GBM model were 0.94, 0.97, 0.90, 0.94, and 0.95, respectively. A GBM ML model contributes to distinguishing SVT from VT based on surface ECG features. In addition, we were able to identify important indicators for distinguishing WQCT.