Decision Tree Research Articles

Using feature selection methods to identify important factors of in-game consumption

ABSTRACT In-game consumption prediction remains under-researched. How to design products that satisfy the needs of users and attract more consumers has also become one of important issues for game App developers. Therefore, this study aims to integrate Technology Acceptance Model into Innovation Theory Diffusion (IDT) to define potential factors that influence users to make in-app purchases. Support Vector Machine -Recursive Feature Elimination (SVM-RFE), Decision Trees (DT), and Information Gain (IG) were utilized to identify the key factors. Perceived usefulness, perceived ease of use, result demonstrability, mass media, perceived enjoyment, cognitive concentration, use context are identified as important factors for in-game purchase.

DT-fuzzy DEMATEL: a new method for determining important factors for student satisfaction with e-course adoption in a Vietnamese university

PurposeAs emerging e-course providers after the COVID-19 crisis, universities (UNI) policymakers in the Mekong Delta region (MDR) have faced difficulties owing to limited clues about what factors improve student retention and recruitment. This study aims to determine important factors (IF) for student satisfaction with e-course adoption (e-satisfaction) for student retention and recruitment.Design/methodology/approachSurvey data collected from 850 students of the target UNI were analyzed using the DT-fuzzy DEMATEL method. Input factor dimensions included course design, technical infrastructure, interaction, teacher-related and student-related factors. Decision Trees (DT) confirmed the final factors; fuzzy decision-making trial and evaluation laboratory (DEMATEL) was used to establish the cause-effect relationships among these factors.FindingsDT-fuzzy DEMATEL method can identify satisfied and dissatisfied students (accuracy = 94.95%) and determine IFs successfully. The most IFs included new and useful knowledge/information provided, various effective teaching methods and motivation to read provided learning materials.Originality/valueAlthough e-satisfaction has been the focus of theories and practices, e-satisfaction in an emerging region like MDR has been studied here for the first time. Most IFs can be used as predictors for e-satisfaction and serve as a primary reference for UNIs’ policymakers. Several practical suggestions were also provided for the sustainable and long-term development of e-programs.

Development of differential diagnostic models for distinguishing between limb-girdle muscular dystrophy and idiopathic inflammatory myopathy

ObjectiveLimb-girdle muscular dystrophy (LGMD) is usually confused with idiopathic inflammatory myopathy (IIM) in clinical practice. Our study aimed to establish convenient and reliable diagnostic models for distinguishing between LGMD and IIM.MethodsA total of 71 IIM patients, 24 LGMDR2 patients and 22 LGMDR1 patients diagnosed at our neuromuscular center were enrolled. Differences in clinical, laboratory and histopathological characteristics were comprehensively compared. A nomogram and a decision tree were developed to distinguish between LGMD and IIM patients.ResultsCompared to patients with LGMD, IIM patients exhibited a significantly older age of onset, a higher prevalence of cervical flexor weakness and a more commonly diffuse MHC-I expression on muscle pathology. The ratio of synchronous serum myoglobin (Mb, ng/ml) to creatine kinase (CK, U/L) before immunotherapy was significantly higher in IIM patients than in LGMD patients. Receiver operating characteristic analysis indicated a high differential diagnostic efficiency of synchronous Mb/CK with a cutoff value of 0.18. A nomogram prediction model and a decision tree were developed based on four independent indicators (age of onset, cervical flexor weakness, synchronous Mb/CK and diffuse MHC-I expression). Five-fold cross-validation and bootstrapping techniques substantiated the discriminate efficacy of the nomograph and decision tree.ConclusionWe developed two practical differential diagnosis models for LGMD and IIM based on the analysis of four accessible indicators, including the age of onset, cervical flexor weakness, the ratio of synchronous Mb/CK values and diffuse MHC-I expression. Further studies with larger samples are needed to refine the predictive efficiency of the differential diagnostic models.

Sound insulation prediction and optimization of wooden support structure for high-speed train floor based on machine learning

In order to improve the sound insulation performance of high-speed train floors, this study first obtained the necessary data for model training based on the reverberation test method, and then conducted data sorting and feature selection. Next, the maximum mutual information minimum redundancy (mRMR) feature selection algorithm was used to calculate the selected features and screen out a subset of significant features. Subsequently, the decision tree, BP neural network, and support vector machine regression (SVR) methods were applied in sequence, and the standardized feature data were used for the high-speed train floor under the same evaluation criteria of the mean square error (MSE) and coefficient of determination (R2). We conducted training and validation of the sound insulation prediction models for timber-framed support structures. The prediction accuracy of the trained model was compared and evaluated with the finite element statistical energy analysis (FE-SEA) prediction model. Finally, the SVR model was used to optimize the design under constraint conditions. The research results show that based on the research object, sample library, and model training in this article, compared with the FE-SEA model, the prediction error of the SVR model is only 0.3 dB, showing better performance. In engineering practice, the SVR model can effectively optimize the wooden support structure in the floor under certain constraints, and it predicts that the weighted sound insulation of the entire floor is 50.45 dB, which has important engineering application value.

Predictive properties of novel anthropometric and biochemical indexes for prediction of cardiovascular risk

ObjectiveOur aim was to examine the correlation between CVDs and various anthropometric and biochemical indices in the Iranian population.Methods9704 healthy individuals without CVD aged 35–65 were enrolled in our study. The anthropometric indices including Body Adiposity Index (BAI), Abdominal Volume Index (AVI), Body Roundness Index (BRI), Waist to Hip Ratio (WHR), Weight-adjusted Waist Index (WWI), Conicity Index (C-Index), A Body Shape Index (ABSI), Waist to Height Ratio (WHtR), Body Surface Area (BSA), Body Mass Index (BMI), Lipid Accumulation Product (LAP) and Visceral Adiposity Index (VAI) were calculated. The biochemical indices including Cardiac Risk Ratio (CRR), Atherogenic Index of Plasma (AIP), Triglycerides-Glucose Index (TyG), Cardiac Risk Index (CRI), Atherogenic Coefficient (AC), and high-sensitivity C-Reactive Protein (hs-CRP) were investigated. The association of the above indices with CVD was analyzed using logistic regression (LR) and the decision tree (DT) models.ResultsThe LR showed age, hs-CRP, AIP, AVI, LAP, and TyG had significant associations with CVDs in men (p-value < 0.002). Also, age, hs-CRP, LAP, TyG, BRI, VAI, and CRR had significant associations with CVDs in women (p-value < 0.002). The DT showed 95% of men with age > = 48, AIP > = 0.94, TyG > = 9.71, and AVI > = 14.24 had CVDs. Also, 97% of women with age > = 54, TyG > = 8.33, and hs-CRP > = 36.69 had CVDs.ConclusionAge, TyG, AIP, AVI, hs-CRP and LAP were the best predictors of CVD in men. Moreover, age, TyG, hs-CRP and BAI were the best indicators of CVD in women.Graphical

Comparing pulsed field ablation and thermal energy catheter ablation for paroxysmal atrial fibrillation: A cost-effectiveness analysis of the ADVENT trial

Background Pulsed field ablation (PFA) has emerged as an effective technology in the treatment of paroxysmal atrial fibrillation (AF). Objective To evaluate the cost-effectiveness of PFA vs. thermal ablation from a US healthcare payer perspective using data from a randomized trial. Methods A hybrid decision tree and Markov model was developed comparing patients receiving PFA to thermal ablation (either radiofrequency or cryoballoon ablation) from a US healthcare payer perspective at 5-, 10-, 20-, and 40-year time horizons. Direct medical costs (in 2024 US Dollars), quality-adjusted life years (QALYs), and the net monetary benefit were evaluated at a willingness-to-pay (WTP) threshold of $100,000/QALY. Univariate and probabilistic sensitivity analyses were performed to test model uncertainty. The budget impact for a standard US healthcare payer with 1 million beneficiaries was also assessed. Results Over a 40-year time horizon, PFA resulted in an additional 0.044 QALYs at a lower cost of $2,871 compared to thermal ablation. PFA was cost-effective in 54.9% of simulations. Anticoagulation and ablation procedure costs had the largest impact on model uncertainty. The expected cost savings per member per month for a US healthcare payer adopting PFA were $0.00015, $0.0059, and $0.02343 in years 1, 4, and 6, respectively. Conclusions PFA was at least as cost-effective as conventional thermal ablation modalities for treatment of paroxysmal AF and potentially reduces US healthcare payer costs. Providers and payers should consider designating PFA among the preferred first-line therapies for eligible patients.

Optimizing Femtosecond Texturing Process Parameters Through Advanced Machine Learning Models in Tribological Applications

Surface texturing plays a vital role in enhancing tribological performance, reducing friction and wear, and improving durability in industrial applications. This study introduces an innovative approach by employing machine learning models—specifically, decision trees, support vector machines, and artificial neural networks—to predict optimal femtosecond laser surface texturing parameters for tungsten carbide tested with WS2 and TiCN coatings. Traditionally, the selection of laser parameters has relied heavily on a trial-and-error method, which is both time-consuming and inefficient. By integrating machine learning, this study advances beyond conventional methods to accurately predict the depth and quality of textured features. The ANN demonstrated superior predictive accuracy among the models tested, outperforming SVM and Decision Trees. This machine learning-based approach not only optimizes the surface texturing process by reducing experimental effort but also enhances the resultant surface performance, making it well-suited for applications in sectors such as automotive and oil and gas.

Exploring machine learning algorithms to predict not using modern family planning methods among reproductive age women in East Africa

BackgroundThe use of the modern family planning method provides chances for women to reach optimal child spacing, increase quality of life, increase economic status, achieve the desired family size, and prevent unsafe abortions and maternal and perinatal deaths. However, use modern family planning is low among reproductive age women in East Africa. Therefore, this study aimed at predicting not using modern family planning and identifying its associated factors among reproductive age women in east Africa using machine learning methods.MethodsThe data set was obtained from a demographic health survey in East Africa. This study used data on weighted sample of 92,564 women who were in their reproductive age. Python software was utilized for data processing, and machine learning models such as Random Forest (RF), Decision Tree (DT), XGB (Extreme Gradient Boosting), SVM (Support Victor Machine), and K-Nearest Neighbor Decision Tree (DT) were implemented. In this work, we evaluated the predictive models’ performance using performance assessment criteria such as accuracy, precision, recall, and the AUC curve.ResultIn this study, 92,564 reproductive women’s was used in the final analysis. Among the proposed machine learning models, XGB performed best overall in the proposed classifier, with an accuracy of 98.7%, precision of 99.8%, recall of 98%, 99.9% AUC score, had high specificity 98%, sensitivity 99.8%, and low error rate discovery was 0.013%. The most significant determinants of not using modern family planning in East Africa: lack of education, age 25–29, living in a rural area, being single, not knowing how to use a contraceptive, and smoking cigarette.ConclusionExtreme Gradient Boosting proved effective in predicting not using modern family planning, offering valuable insights for interventions and policy decisions in East Africa. Machine learning could help achieve early prediction and intervention on reproductive women’s not using modern family planning. This leads to a recommendation for policy direction to reduce child and maternal mortality and improve living standards in east Africa.

Urban Built Environment as a Predictor for Coronary Heart Disease—A Cross-Sectional Study Based on Machine Learning

The relationship between coronary heart disease (CHD) and complex urban built environments remains a subject of considerable uncertainty. The development of predictive models via machine learning to explore the underlying mechanisms of this association, as well as the formulation of intervention policies and planning strategies, has emerged as a pivotal area of research. A cross-sectional dataset of hospital admissions for CHD over the course of a year from a hospital in Dalian City, China, was assembled and matched with multi-source built environment data via residential addresses. This study evaluates five machine learning models, including decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and support vector machine (SVM), and compares them with multiple linear regression models. The results show that DT, RF, and XGBoost exhibit superior predictive capabilities, with all R2 values exceeding 0.70. The DT model performed the best, with an R2 value of 0.818, and the best performance was based on metrics such as MAE and MSE. Additionally, using explainable AI techniques, this study reveals the contribution of different built environment factors to CHD and identifies the significant factors influencing CHD in cold regions, ranked as age, Digital Elevation Model (DEM), house price (HP), sky view factor (SVF), and interaction factors. Stratified analyses by age and gender show variations in the influencing factors for different groups: for those under 60 years old, Road Density is the most influential factor; for the 61–70 age group, house price is the top factor; for the 71–80 age group, age is the most significant factor; for those over 81 years old, building height is the leading factor; in males, GDP is the most influential factor; and in females, age is the most influential factor. This study explores the feasibility and performance of machine learning in predicting CHD risk in the built environment of cold regions and provides a comprehensive methodology and workflow for predicting cardiovascular disease risk based on refined neighborhood-level built environment factors, offering scientific support for the construction of sustainable healthy cities.

Early diagnosis of Alzheimer’s disease and mild cognitive impairment using MRI analysis and machine learning algorithms

Early diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) is crucial to prevent their progression. In this study, we proposed the analysis of magnetic resonance imaging (MRI) based on features including; hippocampus (HC) area size, HC grayscale statistics and texture features (mean, standard deviation, skewness, kurtosis, contrast, correlation, energy, homogeneity, entropy), lateral ventricle (LV) area size, gray matter area size, white matter area size, cerebrospinal fluid area size, patient age, weight, and cognitive score. Five machine learning classifiers; K-nearest neighborhood (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), and multi-layer perception (MLP) were used to distinguish between groups: cognitively normal (CN) vs AD, early MCI (EMCI) vs late MCI (LMCI), CN vs EMCI, CN vs LMCI, AD vs EMCI, and AD vs LMCI. Additionally, the correlation and dependence were calculated to examine the strength and direction of association between each extracted feature and each classification of the group. The average classification accuracies in 20 trials were 95% (SVM), 71.50% (RF), 82.58% (RF), 84.91% (SVM), 85.83% (RF), and 85.08% (RF), respectively, with the best accuracies being 100% (SVM, RF, and MLP), 83.33% (RF), 91.66% (RF), 95% (SVM, and MLP), 96.66% (RF), and 93.33% (DT). Cognitive scores, HC and LV area sizes, and HC texture features demonstrated significant potential for diagnosing AD and its subtypes for all groups. RF and SVM showed better performance in distinguishing between groups. These findings highlight the importance of using 2D-MRI to identify key features containing critical information for early diagnosis of AD.

Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease

BackgroundTo investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.MethodsWe selected 575 elderly patients with CHD admitted to the Affiliated Lu’an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients). Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. ROC curves and calibration plots were used to evaluate the prediction performance of the model. For external validation, 143 patients who were admitted between February and June 2023 from a different associated hospital in Lu'an City were also used.ResultsThe XGBoost model demonstrated the most accurate prediction performance out of the five machine learning techniques. Diabetes, Red blood cell distribution width (RDW), and Triglyceride glucose-body mass index (TyG-BMI), as determined by Lasso regression and multivariate logistic regression. Calibration plot analysis demonstrated that the XGBoost model maintained strong calibration performance across both training and testing datasets, with calibration curves closely aligning with the ideal curve. This alignment signifies a high level of concordance between predicted probabilities and observed event rates. Additionally, decision curve analysis highlighted that both decision trees and XGBoost models achieved higher net benefits within the majority of threshold ranges, emphasizing their significant potential in clinical decision-making processes. The XGBoost model's area under the ROC curve (AUC) reached 0.903, while the external validation dataset yielded an AUC of 0.891, further validating the model's predictive accuracy and its ability to generalize across different datasets.ConclusionTyG-BMI, RDW, and diabetes mellitus at the time of admission are the factors affecting readmission of elderly patients with coronary artery disease, and the model constructed based on the XGBoost algorithm for readmission risk prediction has good predictive efficacy, which can provide guidance for identifying high-risk patients and timely intervention strategies.

Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals

IntroductionAssessing the olfactory preferences of drivers can help improve the odor environment and enhance comfort during driving. However, the current evaluation methods have limited availability, including subjective evaluation, electroencephalogram, and behavioral action methods. Therefore, this study explores the potential of autonomic response signals for assessing the olfactory preferences.MethodsThis paper develops a machine learning model that classifies the olfactory preferences of drivers based on physiological signals. The dataset used for training in this study comprises 132 olfactory preference samples collected from 33 drivers in real driving environments. The dataset includes features related to heart rate variability, electrodermal activity, and respiratory signals which are baseline processed to eliminate the effects of environmental and individual differences. Six types of machine learning models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes) are trained and evaluated on this dataset.ResultsThe results demonstrate that all models can effectively classify driver olfactory preferences, and the decision tree model achieves the highest classification accuracy (88%) and F1-score (0.87). Additionally, compared with the dataset without baseline processing, the model’s accuracy increases by 3.50%, and the F1-score increases by 6.33% on the dataset after baseline processing.ConclusionsThe combination of physiological signals and machine learning models can effectively classify drivers' olfactory preferences. Results of this study can provide a comprehensive understanding on the olfactory preferences of drivers, ultimately enhancing driving comfort.

An intelligent predictive maintenance system based on random forest for addressing industrial conveyor belt challenges

This study introduces a sophisticated intelligent predictive maintenance system for industrial conveyor belts powered by a random forest machine learning model. The random forest model was evaluated against established models such as logistic regression, neural networks, decision trees, and gradient boosting, demonstrating superior performance. The model achieved 100% accuracy in classifying gearbox lubricant levels and sprocket conditions, highlighting its potential for addressing critical challenges in predictive maintenance, such as avoiding unexpected downtime. However, further validation with larger datasets and varied operational environments is recommended to confirm robustness. This performance highlights its effectiveness in multiclass fault detection and overfitting mitigation, establishing a new standard in predictive maintenance technology. The system, enhanced by a comprehensive sensor array, not only adeptly captures but also intelligently analyzes critical operational data, providing proactive and data-driven insights for maintenance decision-making. This study not only affirms the dominance of the random forest model in predictive analytics but also underscores its pivotal role in optimizing maintenance strategies, enhancing operational efficiency, and ensuring the reliability of conveyor systems in industrial settings.

Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context

BackgroundAnemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers promising avenues for improved anemia detection and management. This study investigates the potential of ML models in predicting anemia severity among pregnant women attending Antenatal Care (ANC) visits in Ethiopia.MethodsData from the Ethiopian Demographic Health Survey, specialized hospitals, and public hospitals were utilized. The dataset included individuals diagnosed with severe (65.12%), moderate (15.63%), mild (16.65%) anemia, and non-anemic (2.61%) cases. Feature selection employed filter methods based on mutual information, and F-score was used to assess anemia severity prediction across four classes. Six ML models (MLP-NN, XGBoost, GNB, Decision Tree, Random Forest, and KNN) were evaluated using accuracy, precision, recall, and F1-score.ResultsThe Random Forest classifier achieved the best overall performance across all categories, with an accuracy of 97%, precision of 93%, recall of 93%, and F1-score of 93%. This indicates high true positive rates and low false positive rates. While other models like XGBoost, MLP-NN, and Decision Tree showed good performance, they weren’t quite as strong as Random Forest. Classifiers like KNN and GNB had lower overall accuracy and a tendency to misclassify some cases.ConclusionsThis study demonstrates the promising potential of Random Forest in predicting anemia severity among pregnant women in Ethiopia. The findings contribute to a more holistic understanding of anemia risk factors and pave the way for improved early detection and targeted interventions.

Low-carbohydrate diet score and chronic obstructive pulmonary disease: a machine learning analysis of NHANES data

BackgroundRecent research has identified the Low-Carbohydrate Diet (LCD) score as a novel biomarker, with studies showing that LCDs can reduce carbon dioxide retention, potentially improving lung function. While the link between the LCD score and chronic obstructive pulmonary disease (COPD) has been explored, its relevance in the US population remains uncertain. This study aims to explore the association between the LCD score and the likelihood of COPD prevalence in this population.MethodsData from 16,030 participants in the National Health and Nutrition Examination Survey (NHANES) collected between 2007 and 2023 were analyzed to examine the relationship between LCD score and COPD. Propensity score matching (PSM) was employed to reduce baseline bias. Weighted multivariable logistic regression models were applied, and restricted cubic spline (RCS) regression was used to explore possible nonlinear relationships. Subgroup analyses were performed to evaluate the robustness of the results. Additionally, we employed eight machine learning methods—Boost Tree, Decision Tree, Logistic Regression, MLP, Naive Bayes, KNN, Random Forest, and SVM RBF—to build predictive models and evaluate their performance. Based on the best-performing model, we further examined variable importance and model accuracy.ResultsUpon controlling for variables, the LCD score demonstrated a strong correlation with the odds of COPD prevalence. In compared to the lowest quartile, the adjusted odds ratios (ORs) for the high quartile were 0.77 (95% CI: 0.63, 0.95), 0.74 (95% CI: 0.59, 0.93), and 0.61 (95% CI: 0.48, 0.78). RCS analysis demonstrated a linear inverse relationship between the LCD score and the odds of COPD prevalence. Furthermore, the random forest model exhibited robust predictive efficacy, with an area under the curve (AUC) of 71.6%.ConclusionOur study of American adults indicates that adherence to the LCD may be linked to lower odds of COPD prevalence. These findings underscore the important role of the LCD score as a tool for enhancing COPD prevention efforts within the general population. Nonetheless, additional prospective cohort studies are required to assess and validate these results.

Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters

BackgroundTo protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequently surpassing more established techniques. This study aims to utilize machine learning techniques on predictive parameters for challenging airway management.MethodsThis study was cross-sectional. The Shahid Beheshti University of Medical Sciences in Iran’s Loghman Hakim and Shahid Labbafinezhad hospitals provided 622 records in total for analysis. Using the forest of trees approach and feature importance, important features were chosen. The Synthetic Minority Oversampling Technique (SMOTE) and repeated edited nearest neighbor under-sampling were used to balance the data. Using Python and 10-fold cross-validation, seven machine learning algorithms were assessed: Logistic Regression, Support Vector Machines (SVM), Random Forest (INFORMATION-GAIN and GINI-INDEX), Decision Tree, and K-Nearest Neighbors (KNN). Metrics like F-measure, AUC, Recall, Accuracy, Specificity, and Precision were used to evaluate the performance of the model.ResultsTwenty-four important features were chosen from the original 32 features. The under-sampling strategy produced better results than SMOTE. Among the algorithms, KNN (Euclidean, Minkowski) had better performance than other algorithms. The highest values ​​for accuracy, precision, recall, F-measure, and AUC were obtained at 0.87, 0.88, 0.82, 0.85, and 0.87, respectively.ConclusionAlgorithms for machine learning provide insightful information for anticipating challenging airway management. By making it possible to forecast airway difficulties more accurately, these techniques can potentially improve clinical practice and patient outcomes.

Predicting maternal risk level using machine learning models

BackgroundMaternal morbidity and mortality remain critical health concerns globally. As a result, reducing the maternal mortality ratio (MMR) is part of goal 3 in the global sustainable development goals (SDGs), and previously, it was an important indicator in the Millennium Development Goals (MDGs). Therefore, identifying high-risk groups during pregnancy is crucial for decision-makers and medical practitioners to mitigate mortality and morbidity. However, the availability of accurate predictive models for maternal mortality and maternal health risks is challenging. Compared with traditional predictive models, machine learning algorithms have emerged as promising predictive modelling methods providing accurate predictive models.MethodsThis work aims to explore the potential of machine learning (ML) algorithms in maternal risk level prediction using a nationwide maternal mortality dataset from Oman for the first time. A total of 402 maternal deaths from 1991 to 2023 in Oman were included in this study. We utilised principal component analysis (PCA) in the ML algorithms and compared them to the results of model performance without PCA. We employed and compared ten ML algorithms, including decision tree (DT), random forest (RF), K—Nearest Neighbors (KNN), Naïve Bayes (NB), Extreme Gradient Boosting (xgboost), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN). Different metrics, including, accuracy, sensitivity, precision, and the F1- score, were utilised to assess Model performance.ResultsThe results indicated that the RF model outperformed the other methods in predicting the risk level (low or high) with an accuracy of 75.2%, precision of 85.7% and F1- score of 73% after PCA was applied.ConclusionsWe applied several machine learning models to predict maternal risk levels for the first time using real data from Oman. RF outperformed the other algorithms in this classification problem. A reliable estimate of maternal risk level would facilitate intervention plans for medical practitioners to reduce maternal death.

Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data

In recent years, the new energy vehicle industry has developed rapidly. A fast diagnostic method based on Boosting and big data is proposed to address the low accuracy and efficiency of fault diagnosis in new energy vehicle power batteries. Boosting is a machine learning technique that combines multiple weak learners into a strong learner. Big data refers to large-scale, complex datasets that exceed traditional data processing capabilities. Firstly, analyze and preprocess the big data uploaded by the battery. Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). The experimental results indicate that the LightGBM model effectively detects anomalies in battery big data. The accuracy values of XGBoost, CatBoost, and LightGBM are 97.84%, 98.57%, and 99.16%, respectively. The recall rates of XGBoost, CatBoost, and LightGBM models are all 1. The F1 values of GBoost, CatBoost, and LightGBM are 0.873, 0.983, and 0.985, respectively. The power battery is the core component of new energy vehicles, and its safety performance directly affects the operational safety of the vehicle. Timely identification and diagnosis of battery faults can effectively reduce potential accidents such as battery overheating and short circuits. Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this method can effectively reduce the risk of serious safety accidents and improve the overall operational reliability of new energy vehicles during driving.

Probing Heavy Charged Higgs Boson Using Multivariate Technique at Gamma-Gamma Collider

Abstract he current study explores the production of charged Higgs particles through photon-photon collisions&amp;#xD;within the context of the Two Higgs Doublet Model, including one-loop-level scattering amplitudes of&amp;#xD;Electroweak and QED radiation. The cross-section has been scanned for plane (mϕ0 , √s) investigating&amp;#xD;the process of γγ → H+H−. Three particular numerical scenarios i.e., low-mH , non-alignment, and short-&amp;#xD;cascade are employed. The decay channels for charged Higgs particles are examined using h0 for low-mH0&amp;#xD;and H0 for non-alignment and short-cascade scenario incorporating the new experimental and theoretical&amp;#xD;constraints along with the analysis for cross-sections. It reveals that at low energy, it is consistently higher&amp;#xD;for all scenarios. However, as √s increases, it reaches a peak value at 1 TeV for all benchmark scenarios.&amp;#xD;The branching ratio of the decay channels indicates that for non-alignment, the mode of decay W ±h0 takes&amp;#xD;control, and for short cascade, the prominent decay mode remains t¯b, while in the low-mH the dominant&amp;#xD;decay channel is of W ±h0. In our research, we employ contemporary machine-learning methodologies&amp;#xD;to investigate the production of high energy Higgs bosons within a 3.0 TeV γγ collider. We have used&amp;#xD;multivariate approaches such as Boosted Decision Trees (BDT), LikelihoodD, and Multilayer Perceptron&amp;#xD;(MLP) to show the observability of heavy-charged Higgs Bosons versus the most significant Standard Model&amp;#xD;backgrounds. The purity of the signal efficiency and background rejection are measured for each cut value.Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd

Economic evaluation of childhood pneumococcal conjugate vaccination programs in Qinghai and Hainan provinces, China

ABSTRACT Background The burden of pneumococcal disease varies regionally in China, particularly affecting children in Qinghai and Hainan. Nevertheless, absence of pneumococcal conjugate vaccine (PCV) in the National Immunization Program (NIP) or local programs presents limited coverage. This study evaluated the health and economic impact of including PCV in immunization programs in Qinghai and Hainan. Research design and methods A decision tree Markov model was constructed to simulate health outcomes and lifetime costs among children under different 13-valent PCV (PCV13) vaccination strategies compared to current practices, from societal and healthcare perspectives. Data on epidemiology, vaccine efficacy, cost, and utility were obtained from literature or open databases. Sensitivity analyses were conducted to explore parameter uncertainty. Results Including three-dose PCV13 in provincial programs at NIP coverage (98.91%) could avert 7100 episodes and 118 deaths in Qinghai, and 6200 episodes and 66 deaths in Hainan. It was cost-effective at the $68.2/dose in private market and cost-saving at the United Nations Children’s Fund (UNICEF) recommended $25/dose for both provinces. Increasing coverage to 50% or 75% was also cost-effective. Sensitivity analyses confirmed results robustness despite parameter uncertainty. Conclusions Prioritizing PCV13 in immunization programs in Qinghai and Hainan could effectively reduce disease burden, improve population health, and promote health equity.