Ensemble Learning for Fetal Health Classification

Pulse wave velocity (PWV) can evaluate potential atherosclerosis (AS) and ultrafast pulse wave velocity (ufPWV) is a new technique to accurately assess PWV. However, few studies have examined the predictive value of ufPWV for AS risk. We aimed to establish a classification model for AS risk diagnosis based on ufPWV, so that AS can be diagnosed and prevented in advance. We collected imaging data, as well as clinical and laboratory data. A total of 613 patients with 20 attributes were admitted in this study. There were 392 patients with hyperlipidemia (AS risk group) and 221 healthy adults as the control group. In order to build AS risk prediction models, we considered decision tree, five different ensemble learning (EL) models [random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM)] and two different feature selection methods [statistical analysis and RF]. Accuracy and the area under the ROC curve (AUC) were used as the main criterion for model evaluation. In the prediction of AS risk with statistical analysis as the feature selection method, the performances of XGBoost (accuracy: 0.851; AUC: 0.884) and RF (accuracy: 0.844; AUC: 0.889) were better than other models. Besides, in the prediction of AS risk with RF as the feature selection method, the performances of LGBM (accuracy: 0.870; AUC: 0.903) and XGBoost (accuracy: 0.857; AUC: 0.903) were better than other models. In conclusions, EL models with RF as the feature selection method might provide accurate results in predicting AS risk. Besides, ufPWV, especially PWV of left common carotid artery at the end of systole, was an important feature in the AS risk prediction models.

A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance

As the Chinese population continues to age, the prevalence of gastric ulcers, a common nutrition and diet-related disorder, is rising among the elderly. Gastric ulcers pose a significant public health challenge in China, yet there is limited research to predict gastric ulcers accurately. Our study aims to employ machine learning algorithms to predict the occurrence of gastric ulcers and develop an online tool to assess the risk of gastric ulcers for elderly individuals, both currently and in the future, while identifying important predictors. We used baseline data from the Chinese Longitudinal Healthy Longevity Survey in 2011 and 2014, with a follow-up endpoint of 2018. We employed nine machine learning algorithms to construct predictive models for gastric ulcers over the next seven years (2011-2018, with 1482 samples) and the next three years (2014-2018, with 2659 samples). Additionally, we utilized cross-sectional data from 2018 (with 13,775 samples) to construct a predictive model for current gastric ulcers. Noninvasive predictors such as demographic, behavioral, nutritional, and physical examination factors were utilized to predict the current and future occurrence of gastric ulcers. In our study, Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LGBM) achieved an accuracy of 0.97 for predicting gastric ulcers over seven years; Logistic Regression, Adaptive Boosting, SVM, RF, Gradient Boosting Machine, LGBM, and K-Nearest Neighbors reached 0.98 for three-year predictions; and SVM, Extreme Gradient Boosting, RF, and LGBM attained 0.95 for current gastric ulcer prediction. We developed MyGutRisk, built on optimal machine learning models, relatively accurately predicts gastric ulcer risk in elderly adults using noninvasive factors like diet and lifestyle. It supports self-assessment via a public link and clinical screening in community health settings to guide preventive measures. However, as a prototype, it requires further validation to ensure accuracy and generalizability across diverse populations and real-world applications.

Climate change is a pressing global challenge, notably impacting sensitive regions like the Alpine area. Its diverse terrain and ecology make it vulnerable to heightened climate risks, including intensified weather extremes due to global warming. Precise local climate predictions are vital for managing risks in vulnerable areas like the Alpine region, necessitating reliable high-resolution climate data and forecasts. Global products often fall short in providing the fine-grained information needed for accurate localized assessments. This work aims to address the critical need for refined, high-resolution seasonal climate forecasts in the Alpine region as a tool to increase the ability to manage and anticipate climate variability and hazardous conditions. The study endeavors to utilize Perfect Prognosis (PP) within Statistical Downscaling (SD), leveraging regression-based Machine Learning (ML) algorithms to enhance the spatial resolution of daily temperature and total precipitation of ECMWF (European Centre for Medium Range Weather Forecasts) SEAS5 (Seasonal Forecast System 5) seasonal forecasts. Four ensemble learning methods — random forest, light gradient-boosting machine (LGBM), Adaptive Boosting (AdaBoost) and Extreme Gradient Boosting (XGBoost) are considered, while CERRA (Copernicus European Regional Reanalysis) reanalysis (5.5 km) is used as reference target. In order to define the optimal ML model and configuration, a preparatory phase is performed in which ML methods are implemented, optimized and inter-compared by considering ERA5 reanalysis predictor fields (~ 31 km) for the training period 1985-2015 and validation period 2016-2020. Initial findings show that LGBM reports better performance in training and validation, demonstrating superior computational speed and efficiency with respect to the others. LGBM reconstructs daily variability with R2 scores of 0.95 for mean temperature and 0.67 for precipitation. Remaining bias as yearly average is -0.05°C fo daily mean temperature and 5.34% for daily precipitation. Other error metrics, e.g., mean absolute error (MAE) and root mean squared error (RMSE) suggest room for improvements, especially in extreme value predictions and annual precipitation averages. LGBM is thus applied and further optimized on SEAS5. The contribution will further elaborate the inter-comparison of ML models and their downscaling skills for seasonal forecasts will be presented and discussed. The expected outcomes of this study in particular will serve as a crucial input of a drought prediction module in the framework of the EU-funded interTwin project. This research has been funded by the European Union through the interTwin project (101058386).

BackgroundThis study aims to develop a advanced machine learning model to predict the fall risk among community-dwelling elders. This study could present actionable advices for early prevention of fall risk.MethodsBetween October and December 2022, 977 older adults from the Hannan District of Wuhan were recruited. Data was collected using structured questionnaires. The sample was randomly split into training (732 participants) and testing (245 participants) sets at a 3:1 ratio. The primary outcome was the occurrence of fall. Five machine learning models—Random Forest (RF), Gradient Boosted Decision Tree (GBDT), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Categorical Features Gradient Boosting (CatBoost)—were evaluated against a Logistic Regression (LR) model. Model performance was assessed using AUC, accuracy, precision, sensitivity, specificity, and F1 score.ResultsAmong the 977 older adults, 195 experienced falls (20.0%). ROC curve analysis showed AUC values of LR, RF, LGBM, GBDT, XGBoost, and CatBoost were, respectively, 0.8390, 0.8632, 0.8614, 0.8544, 0.8705, and 0.8719. CatBoost had the highest AUC, indicating the best predictive performance. SHapley Additive exPlanations (SHAP) analysis identified key features influencing the CatBoost model: history of falls, comorbidities, polypharmacy, sleep disorders, ADL, TUG results, frailty status, and use of assistive devices.ConclusionThe fall risk prediction model for community-dwelling older adults, developed with CatBoost, showed excellent performance and can aid in early clinical assessment and fall prevention.

Machine learning-driven prediction of tensile strength in 3D-printed PLA parts

<abstract><p>In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling (ADASYN) yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while LR, SGDC and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance.</p></abstract>

Determination of malathion content in sorghum grains using hyperspectral imaging technology combined with stacked machine learning models

Vehicle-to-Everything Communication (V2X) technologies aim to meet strict quality-of-service (QoS) requirements of vehicular connectivity applications such as safety message exchange, remote driving, and sensor data sharing. The high reliability requirement is particularly important to enable safety relevant applications. Thus, predicting QoS levels becomes key to ensure the reliability of the connected vehicle applications. Recently, machine learning (ML) algorithms are demonstrated to provide dependable predictions to plan, simulate, and evaluate the performance of vehicular networks. In this paper, we propose ML aided New Radio (NR)-V2X QoS predictions scheme to provide Packet Delivery Ratio (PDR) and throughput predictions with the input of Modulation and Coding Schemes (MCS), distance-to-base station, Signal to Interference plus Noise Ratio (SINR), and packet size. Seven different ML algorithms based prediction models are trained and evaluated by using NR-V2X simulation data. We provide performance comparisons between Support Vector Regression (SVR), Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Light GBM (LGBM) for predicting throughput and PDR. We demonstrate that CatBoost and RF are the best performing algorithms to predict throughput and PDR of NR-V2X networks, respectively.

Predicting Caregiver Burden in Multiple Myeloma: Insights from the Carmma Study Using Machine Learning Models

Low birth weight (LBW) is a major public health concern particularly in low and middle-income countries as it contributes to increased infant mortality and long-term health complications. This study applies and evaluates machine learning (ML) algorithms to predict LBW and identify its key risk factors in Bangladesh. Data were collected from 3,192 complete records of ever-married women aged 15–49 years from the Bangladesh Demographic and Health Survey, 2022. Risk factors for LBW were identified by four feature selection techniques including Boruta-based selection (BFS), LASSO regression, Elastic Net and Random Forest (RF). Six ML algorithms, including Logistic Regression (LR), RF, Decision Tree (DT), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were performed to predict LBW. Model performance was evaluated using accuracy, precision, recall, F1-score, AUC, and ROC analysis. SHAP values were utilized to examine the influence of individual features on the model’s prediction. The prevalence of LBW in Bangladesh was 27.8%. Twelve features were identified and the XGB model outperformed the other models by achieving the highest performance in predicting LBW with an accuracy of 80% and area under the curve of 0.761 in holdout (90:10) cross-validation. SHAP analysis revealed that ‘pregnancy duration’ and ‘division’ were the strongest predictors of LBW risk followed by ‘marriage to first birth interval’ ‘ANC visits’ ‘C-section’ and ‘place of delivery’. These findings demonstrate that XGB can serve as an effective tool for predicting LBW and identifying important risk factors that may guide targeted interventions. The insights generated from this study can support public health strategies aimed at reducing LBW prevalence in Bangladesh.

Phase-separation proteins (PSPs) are a class of proteins that play a role in the process of liquid-liquid phase separation, which is a mechanism that mediates the formation of membranelle compartments in cells. Identifying phase separation proteins and their associated function could provide insights into cellular biology and the development of diseases, such as neurodegenerative diseases and cancer. Here, PSPs and non-PSPs that have been experimentally validated in earlier studies were gathered as positive and negative samples. Each protein's corresponding Gene Ontology (GO) terms were extracted and used to create a 24,907-dimensional binary vector. The purpose was to extract essential GO terms that can describe essential functions of PSPs and build efficient classifiers to identify PSPs with these GO terms at the same time. To this end, the incremental feature selection computational framework and an integrated feature analysis scheme, containing categorical boosting, least absolute shrinkage and selection operator, light gradient-boosting machine, extreme gradient boosting, and permutation feature importance, were used to build efficient classifiers and identify GO terms with classification-related importance. A set of random forest (RF) classifiers with F1 scores over 0.960 were established to distinguish PSPs from non-PSPs. A number of GO terms that are crucial for distinguishing between PSPs and non-PSPs were found, including GO:0003723, which is related to a biological process involving RNA binding; GO:0016020, which is related to membrane formation; and GO:0045202, which is related to the function of synapses. This study offered recommendations for future research aimed at determining the functional roles of PSPs in cellular processes by developing efficient RF classifiers and identifying the representative GO terms related to PSPs.

The effective operation of water injection pumps is vital for enhancing oil recovery in the oil and gas industry. To ensure optimal pump performance and prevent unplanned downtime, this study focused on implementing predictive maintenance strategies. We began by identifying five critical operational parameters—Seal Pressure 1, Seal Pressure 2, Vibration Data for the Drive End (VIB DE), Vibration Data for the Non-Drive End (VIB NDE), and Ampere. These parameters were monitored and analyzed to evaluate their impact on pump performance and maintenance needs. To achieve this, we applied three machine learning algorithms: Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LGBM), and Random Forest. Each algorithm was independently trained and tested on the dataset corresponding to each operational parameter. We assessed their performance using key accuracy metrics, including R squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Following this, we developed an Ensemble model, combining the predictive outputs of XGBoost, LGBM, and Random Forest. The Ensemble model was then applied to the same parameters to evaluate its ability to address the limitations observed in standalone models. The results demonstrated that the Ensemble model consistently delivered superior performance, achieving lower RMSE and MAE values and higher R squared coefficients across all parameters. This study culminates in the validation of the Ensemble model as a robust and reliable approach for predictive maintenance. By leveraging the strengths of multiple algorithms, the Ensemble model offers significant improvements in accuracy and reliability, contributing to more effective maintenance systems for the oil and gas industry.

Sepsis may lead to kidney function decline in patients with chronic kidney disease (CKD), and the deleterious effect may persist in patients who survive sepsis. We used a machine learning approach to predict the risk of end-stage renal disease (ESRD) in sepsis survivors. A total of 11,661 sepsis survivors were identified from a single-center database of 112,628 CKD patients between 2010 and 2018. During a median follow-up of 3.5 years, a total of 1366 (11.7%) sepsis survivors developed ESRD after hospital discharge. We adopted the random forest, extra trees, extreme gradient boosting, light gradient boosting machine (LGBM), and gradient boosting decision tree (GBDT) algorithms to predict the risk of ESRD development among these patients. GBDT yielded the highest area under the receiver operating characteristic curve of 0.879, followed by LGBM (0.868), and extra trees (0.865). The GBDT model revealed the strong effect of estimated glomerular filtration rates <25 mL/min/1.73 m2 at discharge in predicting ESRD development. In addition, hemoglobin and proteinuria were also essential predictors. Based on a large-scale dataset, we established a machine learning model computing the risk for ESRD occurrence among sepsis survivors with CKD. External validation is required to evaluate the generalizability of this model.

This study presents an innovative data-driven approach to optimally design water distribution networks (WDNs). The methodology comprises five key stages: Generation of 600 synthetic WDNs with diverse properties, optimized to determine optimal component diameters; Extraction of 80 topological and hydraulic features from the optimized WDNs using graph theory; preprocessing and preparing the extracted features using established data science methods; Application of six feature selection methods (Variance Threshold, k-best, chi-squared, Light Gradient-Boosting Machine, Permutation, and Extreme Gradient Boosting) to identify the most relevant features for describing optimal diameters; and Integration of the selected features with four machine learning models (Random Forest, Support Vector Machine, Bootstrap Aggregating, and Light Gradient-Boosting Machine), resulting in 24 ensemble models. The Extreme Gradient Boosting-Light Gradient-Boosting Machine (Xg-LGB) model emerged as the optimal choice, achieving R2, MAE, and RMSE values of 0.98, 0.017, and 0.02, respectively. When applied to a benchmark WDN, this model accurately predicted optimal diameters, with R2, MAE, and RMSE values of 0.94, 0.054, and 0.06, respectively. These results highlight the developed model’s potential for the accurate and efficient optimal design of WDNs.