Gradient Boosting Algorithm Research Articles

A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands

Wetlands are a critical component of the landscape for climate mitigation, adaptation, biodiversity, and human health and prosperity. Keeping an eye on wetland vegetation is crucial due to it playing a major role in the planet’s carbon cycle and ecosystem management. By measuring the chlorophyll fluorescence (ChF) emitted by plants, we can get a precise understanding of the current state and photosynthetic activity. In this study, we applied the Extreme Gradient Boost (XGBoost) algorithm to map ChF in the Biebrza Valley, which has a unique ecosystem in Europe for peatlands, as well as highly diversified flora and fauna. Our results revealed the advantages of using a set of classifiers derived from EO Sentinel-2 (S-2) satellite image mosaics to accurately map the spatio-temporal distribution of ChF in a terrestrial landscape. The validation proved that the XGBoost algorithm is quite accurate in estimating ChF with a good determination of 0.71 and least bias of 0.012. The precision of chlorophyll fluorescence measurements is reliant upon determining the optimal S-2 satellite overpass time, which is influenced by the developmental stage of the plants at various points during the growing season. Finally, the model performance results indicated that biophysical factors are characterized by greenness- and leaf-pigment-related spectral indices. However, utilizing vegetation indices based on extended periods of remote sensing data that better capture land phenology features can improve the accuracy of mapping chlorophyll fluorescence.

An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption

In this paper, an improved Extreme Gradient Boosting (XGBoost) algorithm based on the Graph Isomorphic Network (GIN) for predicting the adsorption performance of metal-organic frameworks (MOFs) is developed. It is shown that the graph isomorphic layer of this algorithm can directly learn the feature representation of materials from the connection of atoms in MOFs. Then, XGBoost can be used to predict the adsorption performance of MOFs based on feature representation. In this sense, it is not only possible to achieve end-to-end prediction directly from the structure of MOFs to adsorption performance but also to ensure the accuracy of prediction. The comparison between Grand Canonical Monte Carlo (GCMC) simulation and prediction supports the performance and effectiveness of the proposed algorithm.

An implementation of XGBoost algorithm to estimate effective porosity on well log data

The application of machine learning methods is aimed at providing efficiency and avoiding subjectivity in estimating reservoir porosity data. This study proposes the eXtreme Gradient Boost (XGBoost) algorithm which is known to be effective in providing accurate predictions in a short time for estimating effective porosity. The model was optimized using the GridSearchCV (GS) module, then applied to 7 wells from Damar field, Indonesia with variations in the separation of training and testing data based on the number of wells. The best evaluation results are achieved when uses 6 training wells and one tested well with a model accuracy around 78.36% and training time around 1.29 seconds. An increasing the amount of training data will increase the model performance. All variations of training and testing did not show any indication of overfitting. Therefore, it can be concluded that the XGBoost model can effectively estimate reservoir porosity in the area.

Research on the Pre-Warning Method of Aircraft Long Landing Based on the XGboost Algorithm and Operation Characteristics Clustering

Long landing hazardous events (long landings) are regarded as the most common unsafe events during an aircraft’s landing phase and are significantly influenced by pilots’ leveling operations. This paper proposes a pre-warning method for long aircraft landings based on operation characteristics clustering to better prevent the occurrence of long landing events and develop pre-warning technology for long aircraft landings applicable to actual civil aviation aircraft operations. Based on the quick access recorder (QAR) flight data of a Boeing B737-800 fleet, the Gaussian mixture model (GMM) clustering method was employed to cluster, group, analyze, and evaluate the pilot operation characteristics utilizing the relative indicators of aircraft speed in the takeoff and landing phases as the measurement indices. Moreover, a long landing pre-warning model was developed based on the eXtreme Gradient Boosting (XGBoost) algorithm to account for the overall characteristics of various operations. The complete accuracy, recall ratio, and precision of the long landing pre-warning method based on pilot operation characteristics clustering reached 89.66%, 89.16%, and 92.50%, respectively, in the test of the pre-warning model, demonstrating a significant improvement over those of the pre-warning model without considering the operation characteristics and presenting a more effective pre-warning effect. Optimizing the long landing pre-warning model with pilot operation characteristics can effectively improve the model’s pre-warning capabilities, assist the crew in making accurate decisions, and prevent unsafe events during aircraft landing.

A machine learning approach for risk factors analysis and survival prediction of Heart Failure patients

In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability.

Research on Provincial-Level Soil Moisture Prediction Based on Extreme Gradient Boosting Model

As one of the physical quantities concerned in agricultural production, soil moisture can effectively guide field irrigation and evaluate the distribution of water resources for crop growth in various regions. However, the spatial variability of soil moisture is dramatic, and its time series data are highly noisy, nonlinear, and nonstationary, and thus hard to predict accurately. In this study, taking Jiangsu Province in China as an example, the data of 70 meteorological and soil moisture automatic observation stations from 2014 to 2022 were used to establish prediction models of 0–10 cm soil relative humidity (RHs10cm) via the extreme gradient boosting (XGBoost) algorithm. Before constructing the model, according to the measured soil physical characteristics, the soil moisture observation data were divided into three categories: sandy soil, loam soil, and clay soil. Based on the impacts of various factors on the soil water budget balance, 14 predictors were chosen for constructing the model, among which atmospheric and soil factors accounted for 10 and 4, respectively. Considering the differences in soil physical characteristics and the lagged effects of environmental impacts, the best influence times of the predictors for different soil types were determined through correlation analysis to improve the rationality of the model construction. To better evaluate the importance of soil factors, two sets of models (Model_soil&atmo and Model_atmo) were designed by taking soil factors as optional predictors put into the XGBoost model. Meanwhile, the contributions of predictors to the prediction results were analyzed with Shapley additive explanation (SHAP). Six prediction effect indicators, as well as a typical drought process that happened in 2022, were analyzed to evaluate the prediction accuracy. The results show that the time with the highest correlations between environmental predictors and RHs10cm varied but was similar between soil types. Among these predictors, the contribution rates of maximum air temperature (Tamax), cumulative precipitation (Psum), and air relative humidity (RHa) in atmospheric factors, which functioned as a critical factor affecting the variation in soil moisture, are relatively high in both models. In addition, adding soil factors could improve the accuracy of soil moisture prediction. To a certain extent, the XGBoost model performed better when compared with artificial neural networks (ANNs), random forests (RFs), and support vector machines (SVMs). The values of the correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), mean absolute relative error (MARE), Nash–Sutcliffe efficiency coefficient (NSE), and accuracy (ACC) of Model_soil&atmo were 0.69, 11.11, 4.87, 0.12, 0.50, and 88%, respectively. This study verified that the XGBoost model is applicable to the prediction of soil moisture at the provincial level, as it could reasonably predict the development processes of the typical drought event.

Subsynchronous oscillation analysis method based on broad learning system

This paper presents a method to analyze the Sub Synchronous Oscillation (SSO) problem based on broad learning system (BLS), which can quickly analyze the risk of SSO in a system and take measures. The simulation system is constructed by the eigenvalue analysis method to obtain training data, that solves the problem which is difficult to obtain actual data in SSO problem and makes the model more accurate and the conclusion more convincing. Subsequently, BLS is applied for feature extraction and the prediction model is constructed, the model can show the deep relationship between the oscillation source and topology of the system as well as various operating data, which is difficult to be expressed by formula. The system was tested and validated in the 41-bus system. The feature importance analysis method which comes from eXtreme Gradient Boosting (XGboost) algorithm is proposed and combined with the BLS prediction model, which can analyze the influence of each variable on the prediction results in different series compensation levels, and is validated in the 41-bus system, that makes the results of prediction more interpretable. Finally, the results of simulation show that the model can predict data with high accuracy and give adjustment plans for effective control of the system to avoid the risk of SSO.

The role of booster vaccination in decreasing COVID-19 age-adjusted case fatality rate: Evidence from 32 countries.

The global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR. Cross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1-30% in each country. Overall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03-0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs. Booster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

Predicting case difficulty in endodontic microsurgery using machine learning algorithms

ObjectivesThe study aimed to develop and validate machine learning models for case difficulty prediction in endodontic microsurgery, assisting clinicians in preoperative analysis. MethodsThe cone-beam computed tomographic images were collected from 261 patients with 341 teeth and used for radiographic examination and measurement. Through linear regression (LR), support vector regression (SVR), and extreme gradient boosting (XGBoost) algorithms, four models were established according to different loss functions, including the L1-loss LR model, L2-loss LR model, SVR model and XGBoost model. Five-fold cross-validation was applied in model training and validation. Explained variance score (EVS), coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE) and median absolute error (MedAE) were calculated to evaluate the prediction performance. ResultsThe MAE, MSE and MedAE values ​​of the XGBoost model were the lowest, which were 0.1010, 0.0391 and 0.0235, respectively. The EVS and R2 values ​​of the XGBoost model were the highest, which were 0.7885 and 0.7967, respectively. The factors used to predict the case difficulty in endodontic microsurgery were ordered according to their relative importance, including lesion size, the distance between apex and adjacent important anatomical structures, root filling density, root apex diameter, root resorption, tooth type, tooth length, root filling length, root canal curvature and the number of root canals. ConclusionsThe XGBoost model outperformed the LR and SVR models on all evaluation metrics, which can assist clinicians in preoperative analysis. The relative feature importance provides a reference to develop the scoring system for case difficulty assessment in endodontic microsurgery. Clinical significancePreoperative case assessment is a crucial step to identify potential risks and make referral decisions. Machine learning models for case difficulty prediction in endodontic microsurgery can assist clinicians in preoperative analysis efficiently and accurately.

Computation of energy across the type-C piano key weir using gene expression programming and extreme gradient boosting (XGBoost) algorithm

An accurate assessment of energy loss across dams and weir systems is a critical technical and monetary remedy for understanding the hydraulic system’s downstream morphology during the flood. Using the standard empirical formulas, accurately estimating the energy loss across the different hydraulic devices is a tedious and challenging procedure. Consequently, new and concise techniques remain highly sought after. This paper presents two empirical models based on the Gene Expression Programming (GEP) and Extreme Gradient Boosting (XGBoost) techniques to examine energy losses throughout the type-C PKW. The empirical models have been developed to consider five non-dimensional parameters, viz L/W, Ht/P, Wi/Wo, N, and Si/So, that influence the energy over the weir significantly. The models were created using experimental data from a wide range of residual energy losses and release capacities. Additionally, the adequacy of the constructed GEP and XGBoost models was assessed using the RMSE (root mean square error) and statistical variables coefficient (R2). As per the outcomes, the XGBoost model beats the GEP with the determination coefficient (R2) = 0.999, MAE = 0.0062, MAPE = 1.4% and RMSE = 0.0012 in the training stage and R2=0.998, MAE = 0.001, MAPE = 2.1, and RMSE = 0.001 in the testing data. These findings show that the XGBoost algorithm is more accurate than the two algorithms in this study for downstream energy prediction of type-C PKW.

Development of autogenous shrinkage prediction model of alkali-activated slag-fly ash geopolymer based on machine learning

This paper developed an autogenous shrinkage prediction tool with high accuracy through machine learning for alkali-activated slag-fly ash geopolymer. The influencing factors of autogenous shrinkage of activated slag-fly ash geopolymer paste and mortar were analyzed. The results show that Extreme Gradient Boosting (XGB) algorithm achieves the best prediction performance with R2 of over 0.90 and strong generalization ability for predicting the autogenous shrinkage of alkali-activated slag-fly ash geopolymer paste and mortar. The decrease in W/B, alkali dosage and slag content can reduce the autogenous shrinkage of alkali-activated slag-fly ash geopolymer paste, while increasing W/B and decreasing alkali dosage are beneficial to mitigate the autogenous shrinkage of alkali-activated slag-fly ash geopolymer mortar. The Graphical User Interface (GUI) used for autogenous shrinkage prediction of alkali-activated slag-fly ash geopolymer paste or mortar was designed, which can directly be used for predicting autogenous shrinkage on the premise of knowing the synthesis parameters of geopolymer. This prediction tool can prejudge the autogenous shrinkage of alkali-activated slag-fly ash geopolymer materials instead of preforming cumbersome autogenous shrinkage test, which will significantly reduce the workload if we only need to know the autogenous shrinkage.

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixel-wise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.

A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA

Abstract. Geoscientific models are simplified representations of complex earth and environmental systems (EESs). Compared with physics-based numerical models, data-driven modeling has gained popularity due mainly to data proliferation in EESs and the ability to perform prediction without requiring explicit mathematical representation of complex biophysical processes. However, because of the black-box nature of data-driven models, their performance cannot be guaranteed. To address this issue, we developed a generalizable framework for improving the efficiency and effectiveness of model training and the reduction of model overfitting. This framework consists of two parts: hyperparameter selection based on Sobol global sensitivity analysis and hyperparameter tuning using a Bayesian optimization approach. We demonstrated the framework efficacy through a case study of daily edge-of-field (EOF) runoff predictions by a tree-based data-driven model using the extreme gradient boosting (XGBoost) algorithm in the Maumee domain, USA. This framework contributes towards improving the performance of a variety of data-driven models and can thus help promote their applications in EESs.

Identifying Group A Streptococcal Pharyngitis in Children Through Clinical Variables Using Machine Learning.

Background Group A Streptococcus(GAS) is the most common bacterial cause of pharyngitis in children.GAS pharyngitis requires antimicrobial agents, and rapid antigen detection tests (RADTs) are currently considered useful for diagnosis. However, the decision to perform the test is based on the pediatrician's examination findings, but the indicators are not clear. Therefore, we used machine learning (ML) to create a model to identify GAS pharyngitis from clinical findings and to explore important features. Methods ML with Python programming language was used for this study. Data from the included study involved 676 children aged 3 to 15 years diagnosed with pharyngitis, with positive results on the RADT serving as exposures, and negative results serving as controls. The ML performances served as the outcome. We utilized six types of ML classifiers, namely, logistic regression, support vector machine, k-nearest neighbor algorithm, random forest, an ensemble of them, Voting Classifier, and the eXtreme Gradient Boosting (XGBoost) algorithm. Additionally, we used SHapley Additive exPlanations (SHAP) values to identify important features. Results Moderately performing models were generated for all six ML classifiers. XGBoost produced the best model, with an area under the receiver operating characteristics curve of 0.75 ± 0.01. The order of important features in the model was palatal petechiae, followed by scarlatiniform rash,tender cervical lymph nodes, and age. Conclusion Through this study, we have demonstrated that ML models can predict childhood GAS pharyngitis with moderate accuracy using only commonly recorded clinical variables in children diagnosed with pharyngitis. We have also identified four important clinical variables. These findings may serve as a reference for considering indicators under the current guidelines recommended for selective RADTs.

Intersense: An XGBoost model for traffic regulator identification at intersections through crowdsourced GPS data

Digital maps of the transportation network are the foundation of future mobility solutions. Autonomous and connected vehicles rely on real-time, at-scale updating of the environment in which they operate. Successful operation in a hybrid environment, where human and machine intelligence coexist, requires explicit knowledge of the traffic regulator infrastructure. Future generation traffic management strategies and path planning systems must be tightly integrated with the regulator infrastructure in order to improve traffic dynamics and reduce congestion in urban environments. In this article, we present Intersense, a regulator identification and categorization system that leverages raw, naturalistic GPS trajectory data to infer the existence and type of regulators present at network intersections. A supervised learning approach, based on the eXtreme Gradient Boosting (XGBoost) algorithm, combines infrastructure and vehicle telemetry data features to analyze movement patterns vehicles follow when approaching intersections. For the most widely used regulator types (traffic light, stop sign and right of way), the achieved accuracy surpasses 96%. Permutation feature importance is used to evaluate the relative importance of the generated features, providing a detailed view of the classification model and insights for future data collection and processing stages. We showcase the Intersense’s adaptability to various GPS data sources with uneven penetration and sampling rates and we illustrate the system’s transferability across very diverse cities by performing experiments in San Jose and San Francisco. Finally, the relationship between the number of available trajectories and classification accuracy is derived to determine the necessary data collection investment.

Application of machine learning model based on XGBoost algorithm in early prediction of patients with acute severe pancreatitis

To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca2+ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.

High‐Temperature Exposure and Land Transactions in China

AbstractWe estimate the relationship between high‐temperature exposure and land transaction using daily transaction data from 2013 to 2018 in China. Standard hedonic price method is used to perform multidimensional fixed effects regression on 458,564 transaction samples. This study shows that each additional day of the extremely high temperature increases the average bid price by 0.6%, which is equivalent to an additional annual increase of 15.018 billion yuan in land transaction costs. The effect of high temperature on land prices is considerably stronger for residential and commercial lands. We further find that high‐temperature‐induced changes of mood and cognition play a role. Extreme gradient boosting (XGBoost) algorithm is used to calculate price deviation, which overcome missing eigenvalues and reduce nonlinear measurement error. Our findings indicate that heat can make investors more aggressive, which occurs when bidders continue to raise prices to win. The number of bidding behavior will not drop due to the hidden costs of land transactions. Specially, bidding experience offsets the cognitive output bias. This study contributes to the behavioral finance and decision‐making literature, helps investors make better investment management decisions, and alleviates the negative effect of land transaction premium on the real estate market.

Comprehensive evaluations of individual discrimination, kinship analysis, genetic relationship exploration and biogeographic origin prediction in Chinese Dongxiang group by a 60-plex DIP panel

BackgroundDongxiang group, as an important minority, resides in Gansu province which is located at the northwest China, forensic detection system with more loci needed to be studied to improve the application efficiency of forensic case investigation in this group.MethodsA 60-plex system including 57 autosomal deletion/insertion polymorphisms (A-DIPs), 2 Y chromosome DIPs (Y-DIPs) and the sex determination locus (Amelogenin) was explored to evaluate the forensic application efficiencies of individual discrimination, kinship analysis and biogeographic origin prediction in Gansu Dongxiang group based on the 60-plex genotype results of 233 unrelated Dongxiang individuals. The 60-plex genotype results of 4582 unrelated individuals from 33 reference populations in five different continents were also collected to analyze the genetic background of Dongxiang group and its genetic relationships with other continental populations.ResultsThe system showed high individual discrimination power, as the cumulative power of discrimination (CPD), cumulative power of exclusion (CPE) for trio and cumulative match probability (CMP) values were 0.99999999999999999999997297, 0.999980 and 2.7029E− 24, respectively. The system could distinguish 98.12%, 93.78%, 82.18%, 62.35% and 39.32% of full sibling pairs from unrelated individual pairs, when the likelihood ratio (LR) limits were set as 1, 10, 100, 1000 and 10,000 based on the simulated family samples, respectively. Additionally, Dongxiang group had the close genetic distances with populations in East Asia, especially showed the intimate genetic relationships with Chinese Han populations, which were concluded from the genetic affinities and genetic background analyses of Dongxiang group and 33 reference populations. In terms of the effectiveness of biogeographic origin inference, different artificial intelligent algorithms possessed different efficacies. Among them, the random forest (RF) and extreme gradient boosting (XGBoost) algorithm models could accurately predict the biogeographic origins of 99.7% and 90.59% of three and five continental individuals, respectively.ConclusionThis 60-plex system had good performance for individual discrimination, kinship analysis and biogeographic origin prediction in Dongxiang group, which could be used as a powerful tool for case investigation.

Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning.

Atrioventricular block requiring permanent pacemaker (PPM) implantation is an important complication of transcatheter aortic valve replacement (TAVR). Application of machine learning could potentially be used to predict pre-procedural risk for PPM. To apply machine learning to be used to predict pre-procedural risk for PPM. A retrospective study of 1200 patients who underwent TAVR (January 2014-December 2017) was performed. 964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis. After the exclusion of variables with near-zero variance or ≥ 50% missing data, 167 variables were included in the random forest gradient boosting algorithm (GBM) optimized using 5-fold cross-validations repeated 10 times. The receiver operator curve (ROC) for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year. Of 964 patients included in the 30-d analysis without prior PPM, 19.6% required PPM post-TAVR. The mean age of patients was 80.9 ± 8.7 years. 42.1 % were female. Of 657 patients included in the 1-year analysis, the mean age of the patients was 80.7 ± 8.2. Of those, 42.6% of patients were female and 26.7% required PPM at 1-year post-TAVR. The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model (0.66 and 0.72) was superior to that of the PPM risk score (0.55 and 0.54) with a P value < 0.001. The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.

Unraveling the importance of fabrication parameters of copper oxide-based resistive switching memory devices by machine learning techniques

In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.