Random Tree Model Research Articles

Investigation of Ensemble Learning Method in Predicting Early Xerostomia Using Radiomics Features from CT and MRI

There are different side effects in radiotherapy of head and neck cancer including xerostomia. Xerostomia is due to irradiation of salivary glands during treatment. In the present study, the main aim is evaluation and validation of individual and ensemble classifiers for prediction of early xerostomia in radiotherapy of head and neck cancer. Totally 62 patients diagnosed with head and neck cancer were evaluated prospectively. For this purpose, patients' demographic information, dosimetric data, and the extracted features from CT and T1 and T2 MR images before radiotherapy treatments were used as input for the predictive model. There were two main steps in this study: 1- Comparison of the features from T1 and T2 MR images for prediction of xerostomia, and 2- Evaluation of the performance of the radiomics models using individual classifiers and combination of the classifiers using ensemble learning. Feature extraction was performed using 3D Slicer software and for both parotids, 642 features were extracted from both CT, and MRI and Pearson statistical test was used for feature selection. For model development, four models were used consist of Random Tree (RT), Neural Network (NN), Linear Support Vector Machine (LSVM) and Bayesian Network (BS) classifiers. Different algorithms combined by using the ensemble learning method. For evaluation of the performance of the constructed models, sensitivity, specificity, area under curve (AUC), and receiver operating characteristic (ROC) curve were utilized. The results of the present study show that the extracted features from T1 weighted images have superior prediction ability compared to the T2 weighted ones. The RT and LSVM models which were based on T1 weighted images have better performance than those with T2 weighted images. The AUC in the test group for these models are 0.95 and 0.76, respectively, while for the T2 weighted images the corresponding values are 0.75 and 0.63. The AUC for the test group for the RT-BN, RT-LSVM-BN and RT-NN-LSVM-BN models amount to 0.96, 0.91, and 0.88, respectively. The results show that the radiomics features from images before radiotherapy can be used as personalized and unique biomarkers for prediction of xerostomia. Ensemble classifiers can be more efficient than individual classifiers in prediction of early xerostomia.

Detecting future financial statement fraud using a machine learning model in Indonesia: a comparative study

PurposeThis study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.Design/methodology/approachThis study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.FindingsThe findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.Practical implicationsThis study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.Originality/valueThis study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

Spatial prediction and uncertainty estimation of crucial GlobalSoilMap properties - A contextual study in the semi-arid area of western Iran

Information on the spatial distribution of organic carbon (OC), salinity (EC), and soil pH in the semi-arid region of the Chahardowli Plain in western Iran is limited. Though soil carbon is the most common property mapped in GlobalSoilMap, EC, and pH affect OC content and other soil properties and functions. To study variations in these properties and map their distribution, soil samples were collected at depths of 0–5, 5–15, 15–30, 30–60, and 60–100 cm. A total of 145 soil samples were collected from 30 profiles. The relationships between soil characteristics and environmental covariates were modeled using random forest (RF), decision tree (DT), and multiple linear regression (MLR) models. We used a k-fold cross-validation to assess the quality of the predictions. The RF model demonstrated the highest prediction accuracy for all three soil properties. The OC validation results for the RF model show that the R2 value was between 0.80 and 0.98, the R2 value for EC was between 0.74 and 0.98, and the R2 value for pH was between 0.80 and 0.93. The channel network base level (CNBL) was found to be the most crucial covariate in predicting EC, while CNBL and vegetation indices were the most significant covariates in predicting OC. The covariates found to be crucial in predicting pH were the difference in vegetation index (DVI) and slope (S). The bootstrap method was used to compute the prediction uncertainty. The bootstrap method provided reliable estimates of uncertainties associated with these predictions. For all layers and all points, the coverage percentage for OC, EC, and pH was between 80 and 95% at the 95% confidence level. This shows the reliability of the estimated confidence limits. This study indicated that high pH and EC levels are associated with a reduction in soil OC percentage. To provide an accurate representation of OC distribution in any region, it is necessary to report not only an OC map but also maps of EC and pH, as the spatial interrelationship between soil properties highlights the need for estimating pH and EC for better understanding OC variability and soil functioning.

Defect Classification for Additive Manufacturing with Machine Learning.

Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model's accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process's parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process.

How to convince in a televised debate: the application of machine learning to analyze why viewers changed their winner perception during the 2021 German chancellor discussion

What causes viewers to change their winner perception during a televised debate? The article addresses this question, drawing on a large-N field study of the 2021 chancellor debate in Germany, which contains survey and real-time response data for 4613 participants. Using machine learning techniques, we identify determinants of why participants change their opinion about who is winning the discussion during the debate. Our analysis based on random forest and decision tree models shows in detail, first, what factors drive debate winner perceptions in the course of televised debate reception. Second, we reveal what combinations of political predispositions and candidate statements are necessary to change the viewers’ debate winner perception. In doing so, third, we expand the toolbox of empirical debate research with our analysis based on machine learning algorithms. Our findings indicate that pre-debate chancellor preference and candidate images play a crucial role in determining post-debate perception change, while party identification is less important in predicting changes. We can also directly identify several speech moments in the debate that shifted viewer perception, a novel approach to evaluating political debate performance.

SENTIMENT LABELING AND TEXT CLASSIFICATION MACHINE LEARNING FOR WHATSAPP GROUP

The use of WhatsApp Group (WAG) for communication is increasing nowadays. WAG communication data can be analyzed from various perspectives. However, this data is imported in the form of unstructured text files. The aim of this research is to explore the potential use of the SentiwordNet lexicon for labeling the positive, negative, or neutral sentiment of WAG data from "Alumni94" and training and testing it with machine learning text classification models. The training and testing were conducted on six models, namely Random Forest, Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), and Artificial Neural Network. The labeling results indicate that neutral sentiment is the majority with 7588 samples, followed by 324 negative and 1617 positive samples. Among all the models, Random Forest showed better precision and recall, i.e., 83% and 64%. On the other hand, Decision Tree had slightly lower precision and recall, i.e., 80% and 66%, but exhibited a better f-measure of 71%. The accuracy evaluation results of the Random Forest and Decision Tree models showed significant performance compared to others, achieving an accuracy of 89% in classifying new messages. This research demonstrates the potential use of the SentiwordNet lexicon and machine learning in sentiment analysis of WAG data using the Random Forest and Decision Tree models

A comprehensive analysis of type 1 interferon gene signatures in systematic lupus erythematosus and prediction of the crucial susceptible factor for Sjögren syndrome.

This study aimed to determine the role of IFN-1 gene signatures in SLE and their association with Sjögren syndrome (SS). Publicly available data from the Gene Expression Omnibus database were used to construct the models. The random forest tree model was used to screen key IFN-1 gene signatures, and consensus clustering algorithms were used for unsupervised cluster analysis of these signatures. CIBERSORT and gene set variation analyses were used to evaluate the relative immune cell infiltration and enriched molecular pathways of the samples, respectively. Weighted gene co-expression network analysis was used to identify the co-expression modules and hub genes. Finally, univariate and multivariate logistic regression models were used to evaluate differences in clinical and laboratory characteristics between the different groups. The role of IFN-1 gene signatures in SLE was comprehensively assessed, which revealed an IFN-1 gene signature including six genes that could easily distinguish SLE patients and healthy individuals and identified two distinct IFN-1 subtypes exhibiting significant differences in clinical characteristics, immune microenvironment, and biological functional pathways. The SLE disease activity index, lower lymphocyte count, nucleotide oligomerization domain (NOD)-like receptor signaling pathway, and dendritic cell activation were strongly correlated with the IFN-1 gene signatures. In addition, we found that IFN-1 gene signatures in SLE may be an important susceptibility factor for SS, and the NOD-like receptor signaling pathway was identified as a common pathway. This study provides a comprehensive evaluation of the IFN-1 gene signatures, which may provide a new direction for the understanding of SLE and SS and help in the selection of optimal strategies for personalized immunotherapy.

Bagged stepwise cluster analysis for probabilistic river flow prediction

River flow prediction plays a crucial role in flood control and water resource management. As an effective approach, the stepwise cluster analysis (SCA) has been well-accepted in river flow prediction but still encounters difficulties to acquire accurate predictions in sample-sparse regimes. This study proposes a new ensemble model by incorporating the bagging method with the SCA. The proposed Bagged Stepwise Cluster Analysis (BSCA) is applied to predict daily river flow in the Upper Min River, China. The results show that BSCA effectively improves the prediction accuracy of SCA. Particularly, it addresses the limited end clusters problem of SCA and thus enhances the extreme-value prediction. It also has a better overall prediction performance over the classical random forest and decision tree models. In addition, BSCA is capable of quantifying uncertainties through a probabilistic prediction, which can be used to assess risks of extreme flows and provide reliable river flow predictions. The proposed new model can be applied to other water systems and environmental engineering problems.

The performance comparison of the decision tree models on the prediction of seismic gravelly soil liquefaction potential based on dynamic penetration test

Seismic liquefaction has been reported in sandy soils as well as gravelly soils. Despite sandy soils, a comprehensive case history record is still lacking for developing empirical, semi-empirical, and soft computing models to predict this phenomenon in gravelly soils. This work compiles documentation from 234 case histories of gravelly soil liquefaction from across the world to generate a database, which will then be used to develop seismic gravelly soil liquefaction potential models. The performance measures, namely, accuracy, precision, recall, F-score, and area under the receiver operating characteristic curve, were used to evaluate the training and testing tree-based models’ performance and highlight the capability of the logistic model tree over reduced error pruning tree, random tree and random forest models. The findings of this research can provide theoretical support for researchers in selecting appropriate tree-based models and improving the predictive performance of seismic gravelly soil liquefaction potential.

Characterizing and Predicting the Quality of Milled Rice Grains Using Machine Learning Models

Physical classification is the procedure adopted by the rice unloading, delivery, storage, and processing units for the commercial characterization of the quality of the grains. This step occurs mostly by the conventional method, which demands more time and specialized labor, and the results are subjective since the evaluation is visual. In order to make the operation faster, more accurate, and less dependent, non-destructive technologies and computational intelligence can be applied to characterize grain quality. Therefore, this study aimed to characterize and predict the quality of whole, processed rice grains, as well as classify any defects present. This was achieved by sampling from the upper and lower points of four silo dryers with capacities of up to 40,000 sacks. The grain samples had moisture contents of 16%, 17%, 18%, and 19% and were subjected to drying-aeration until reaching 12% moisture content (w.b.). Near-infrared spectroscopy technology and Machine Learning algorithm models (Artificial Neural Networks, decision tree algorithms Quinlan’s algorithm, Random Tree, REPTree, and Random Forest) were employed for this purpose. By analyzing Pearson’s correlation statistics, a strong negative correlation (R2 = 0.98) was found between moisture content and the yield of whole grains. Conversely, a strong positive correlation (R2 = 0.97) was observed between moisture content and classified physical defects across the various characterized physicochemical constituents. These findings indicate the effectiveness of near-infrared spectroscopy technology. The Random Tree model (RandT) successfully predicted the grain quality outcomes and is therefore recommended as the model of choice, obtained Pearson’s correlation coefficient (r = 0.96), mean absolute error (MAE = 0.017), and coefficient of determination (R2 = 0.92). The results obtained here reveal that the combination of near-infrared spectroscopy technology and Machine Learning algorithm models is an excellent non-destructive alternative to manual physical classification for characterizing the physicochemical quality of whole and defective rice grains.

Estimation of forest canopy closure in northwest Yunnan based on multi-source remote sensing data colla-boration.

Forest canopy closure (FCC) is an important parameter to evaluate forest resources and biodiversity. Using multi-source remote sensing collaborative means to achieve regional forest canopy closure inversion with low cost and high-precision is a research hotspot. Taking ICESat-2/ATLAS data as the main information source and combined with data of 54 measured plots, we estimated FCC value by the Bayesian optimization (BO) algorithm improved random forest (RF), K-nearest neighbor (KNN), and gradient boosting regression tree (GBRT) model at footprint-scale. Combined with multi-source remote sensing image Sentinel-1/2 and terrain factors, we estimated the regional-scale FCC value of Shangri-La in the northwest Yunnan based on deep neural network (DNN) optimized by BO algorithm. The results showed that six characteristic parameters (percentage of tree canopy, standard deviation of relative height of photons at the top of the canopy, minimum canopy height, difference between 98% canopy height and median canopy height in the segment, number of top canopy photons, apparent surface reflectance) out of the 50 parameters that were extracted from ATLAS lidar footprint had higher contribution rate after RF characteristic variable optimization, which could be used as model variable for footprint-scale remote sensing estimation. Among BO-RF, BO-KNN, and BO-GBRT models, the FCC results estimated by the BO-GBRT model were the best at footprint-scale. The coefficient of determination (R2) was 0.65, the root mean square error (RMSE) was 0.10, the mean absolute residual (RS) was 0.079, and the prediction accuracy (P) was 0.792 for leave-one-out cross validation. It could be used as the FCC estimation model of 74808 ATLAS footprints for forest in the study area. We used the ATLAS footprint-scale FCC value of forest as the large sample data of the regional-scale BO-DNN model and combined with multi-source remote sensing factors to estimate FCC in the study area, the accuracy of the 10-fold cross-validation BO-DNN model was R2=0.47, RMSE=0.22, P=0.558. The mean values of FCC in the study area estimated by BO-DNN model and ordinary Kriging (OK) interpolation were 0.46 and 0.52, respectively, and the values mainly distributed in 0.3-0.6, accounting for 77.8% and 81.4%, respectively. The FCC efficiency obtained directly by the OK interpolation method was higher (R2=0.26), but the prediction accuracy was significantly lower than the BO-DNN model (R2=0.49). The FCC high value was distributed from northwest to southeast in the study area, and the northern and southeastern regions were the main distribution areas of high and low FCC values, respectively. It had certain advantages to estimate mountain area FCC based on ICESat-2/ATLAS high-density footprint, and the estimation results of small sample data at footprint-scale could be used as large sample data of deep learning model at region-scale, which would provide a reference for the low-cost and high-precision to FCC estimation on the footprint-scale up to the extrapolated regional-scale.

Bioinformatic Analysis of the Potential Common Pathogenic Mechanisms for Psoriasis and Metabolic Syndrome.

The pathogeneses of psoriasis and metabolic syndrome are closely related; however, the underlying biological mechanisms are yet to be clarified. A psoriasis training set was downloaded from the Gene Expression Omnibus database and analyzed to identify the differentially expressed genes (|logFC|> 1 and adjust P < 0.05). Differentially expressed genes for metabolic syndrome were obtained from the GeneCards, Online Mendelian Inheritance in Man, and DisGeNET databases, and crosstalk genes were obtained for multiple enrichment analysis after identifying the disease intersection. Characteristic crosstalk genes were screened using the least absolute shrinkage and selection operator regression model and random forest tree model, and the genes with area under the receiver operating characteristic curve > 0.7 were selected for validation by the two validation sets. Differential analyses of immune cell infiltration were performed on psoriasis lesion and control samples using the CIBERSORT and ImmuCellAI methods, and correlation analyses were performed between the screened signature crosstalk genes and immune cell infiltration. Significant crosstalk genes were analyzed based on the psoriasis area and severity index and on the responses to biological agents. We found five signature genes (NLRX1, KYNU, ABCC1, BTC, and SERPINB4) were screened based on two machine learning algorithms, and NLRX1 was validated. The infiltration of multiple immune cells in psoriatic lesions and non-lesions was associated with NLRX1 expression. NLRX1 was found to be associated with psoriasis severity and response rate after the use of biologics. NLRX1 could be a significant crosstalk gene for psoriasis and metabolic syndrome.

Research on Identifying Important Factors and Prediction of Online Service Satisfaction for Mobile Phone Users

Our lives cannot do without the internet. How to improve the network quality has always been an essential problem. The paper explores the important factors of online service satisfaction and the best predicting model. Based on the data offered by Beijing Mobile Company, we identify main factors affecting online service satisfaction by calculating their mutual information values. The factors include signal problem factors, scene factors and software usage factors. Additionally, based on decision tree model and models with decision tree as base learner, we predict the online service satisfaction. The result shows that random tree model with One Vs Rest mode has the greatest accuracy among the models which offers telecommunications companies insight.

Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis.

The activation of YAP/TAZ transcriptional co-activators, downstream effectors of the Hippo/YAP pathway, is commonly observed in human cancers, promoting tumor growth and invasion. The aim of this study was to use machine learning models and molecular map based on the Hippo/YAP pathway to explore the prognosis, immune microenvironment and therapeutic regimen of patients with lower grade glioma (LGG). SW1783 and SW1088 cell lines were used as in vitro models for LGG, and the cell viability of the XMU-MP-1 (a small molecule inhibitor of the Hippo signaling pathway) treated group was evaluated using a Cell Counting Kit-8 (CCK-8). Univariate Cox analysis on 19 Hippo/YAP pathway related genes (HPRGs) was performed to identify 16 HPRGs that exhibited significant prognostic value in meta cohort. Consensus clustering algorithm was used to classify the meta cohort into three molecular subtypes associated with Hippo/YAP Pathway activation profiles. The Hippo/YAP pathway's potential for guiding therapeutic interventions was also investigated by evaluating the efficacy of small molecule inhibitors. Finally, a composite machine learning models was used to predict individual patients' survival risk profiles and the Hippo/YAP pathway status. The findings showed that XMU-MP-1 significantly enhanced the proliferation of LGG cells. Different Hippo/YAP Pathway activation profiles were associated with different prognostic and clinical features. The immune scores of subtype B were dominated by MDSC and Treg cells, which are known to have immunosuppressive effects. Gene Set Variation Analysis (GSVA) indicated that subtypes B with a poor prognosis exhibited decreased propanoate metabolic activity and suppressed Hippo pathway signaling. Subtype B had the lowest IC50 value, indicating sensitivity to drugs that target the Hippo/YAP pathway. Finally, the random forest tree model predicted the Hippo/YAP pathway status in patients with different survival risk profiles. This study demonstrates the significance of the Hippo/YAP pathway in predicting the prognosis of patients with LGG. The different Hippo/YAP Pathway activation profiles associated with different prognostic and clinical features suggest the potential for personalized treatments.

Credit Card Default Prediction based on Machine Learning Techniques

In recent years, with the development of society and economy, credit cards have been popularized due to their low interest rate and easy payment. However, with the advent of the epidemic era, the unemployment rate has increased, making the probability of credit card defaults rising. The prediction of credit card default helps banks and financial institutions balance the risk and economic interests, contributes to the stable and healthy development of the financial industry, and plays an important role in bank credit control. Therefore, this paper addresses the credit card default prediction problem by using Random forest, Decision tree, LightGBM, XGBoost, Logistic regression, and Adaboost models to make predictions and compare the results. The outcomes demonstrate that LightGBM algorithm has the most outstanding prediction score, and its AUC value can reach 0.78 and recall rate reaches 0.95.

A diagnostic model based on color vision examination for dysthyroid optic neuropathy using Hardy-Rand-Rittler color plates.

To investigate color vision deficiency and the value of Hardy-Rand-Rittler (HRR) color plates in monitoring dysthyroid optic neuropathy (DON) to improve the diagnosis of DON. The participants were divided into DON and non-DON (mild and moderate-to-severe) groups. All the subjects underwent HRR color examination and comprehensive ophthalmic examinations. The random forest and decision tree models based on the HRR score were constructed by R software. The ROC curve and accuracy of different models in diagnosing DON were calculated and compared. Thirty DON patients (57 eyes) and sixty non-DON patients (120 eyes) were enrolled. The HRR score was lower in DON patients than in non-DON patients (12.1 ± 6.2 versus 18.7 ± 1.8, p < 0.001). The major color deficiency was red-green deficiency in DON using HRR test. The HRR score, CAS, RNFL, and AP100 were found to be important factors in predicting DON from random forest and selected by decision tree to construct the multifactor model. The sensitivity, specificity, and the area under the curve (AUC) of the HRR score were 86%, 72%, and 0.87, respectively. The HRR score decision tree had a sensitivity, specificity, and AUC of 93%, 57%, and 0.75, respectively, with an accuracy of 82%. The data of the multifactor decision tree were 90%, 89%, and 0.93 for sensitivity, specificity, and AUC, respectively, with an accuracy of 91%. The HRR test was valid as screening method for DON. The multifactor decision tree based on the HRR test improved the diagnostic efficacy for DON. An HRR score of less than 12 and red-green deficiency may be characteristic of DON.

Modeling the spatial variation of calcium carbonate equivalent to depth using machine learning techniques

Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this study was to model and map calcium carbonate equivalent (CCE) presenting inorganic carbon in soil using machine learning and digital soil mapping techniques. Chahardowli Plain in foothills of the Zagros Mountains in the southeast of Kurdistan Province in Iran was taken as a case study area. CCE was measured at 0-5, 5-15, 15-30, 30-60, and 60-100cm depths following GloalSoilMap.net project specifications. A total of 145 samples were collected from 30 soil profiles using the conditional Latin hypercube (cLHS) method of sampling. Relationships between CCE and environmental predictors were modeled using random forest (RF) and decision tree (DT) models. In general, the RF model performed slightly superior than the DT model. The mean value of CCE increased with soil depth, from 3.5% (0-5cm) to 63.8% (30-60cm). Remote sensing (RS) variables and terrestrial variables were equally important. The importance of RS variables was higher at the surface than terrestrial variables, and vice versa. The most significant variables were Channel Network Base Level (CNBL) variable and Difference Vegetation Index (DVI) with the same variable importance value (21.1%). In areas affected by river activities, the use of the CNBL and vertical distance to channel networks (VDCN) as variables in digital soil mapping (DSM) could increase the accuracy of soil property prediction maps. The VDCN played a principal role in soil distribution in the study area by affecting the rate of discharge and, thus, erosion and sedimentation. A high percentage of carbonate in parts of the region could exacerbate nutrient deficiencies for most crops and provide information for sustainably managing agricultural activity.

Association of Long-term exposure to air pollution and residential greenness with lipid profile: Mediating role of inflammation

Lipidemic effect of air pollutants are still inconsistent and their joint effects are neglected. Meanwhile, identified inflammation pathways in animal have not been applied in epidemiological studies, and beneficial effect of residential greenness remained unclear. Therefore, we used data from typically air-polluted Chinese cities to answer these questions. Particulate matter (PM) with a diameter of ≤ 1 µm (PM1), PM with a diameter of ≤ 2.5 µm (PM2.5), PM with a diameter of ≤ 10 µm (PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) were predicted by space–time extremely randomized trees model. Residential greenness was reflected by Normalized Difference Vegetation Index (NDVI). Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured, and atherogenic coefficient (AC) and TG/HDL-C (TGH) ratio were calculated to indicate lipid metabolism. Generalized additive mixed model and quantile g-computation were respectively conducted to investigate individual and joint lipidemic effect of air pollutants. Covariates including demographical characteristics, living habits, meteorological factors, time trends, and disease information were considered to avoid confounding our results. Complement C3 and high-sensitivity C-reactive protein (hsCRP) were analyzed as potential mediators. Finally, association between NDVI and lipid markers were explored. We found that long-term air pollutants exposure were positively associated with lipid markers. Complement C3 mediated 54.72% (95% CI: 0.30, 63.10) and 72.53% (95% CI: 0.65, 77.61) of the association between PM1 and TC and LDL-C, respectively. We found some significant associations of lipid markers with NDVI1000 m rather than NDVI500 m. BMI, disease status, smoke/drink habits are important effect modifiers. Results are robust in sensitive analysis. Our study indicated that air pollutants exposure may detriment lipid metabolism and inflammation may be the potential triggering pathways, while greenness may exert beneficial effects. This study provided insights for the lipidemic effects of air pollution and greenness.

Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach.

The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.

A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence

Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.