Integrated machine learning and geospatial analysis enhanced gully erosion susceptibility modeling in the Erer watershed in Eastern Ethiopia

Integrating machine learning (ML) models into healthcare systems is a rapidly evolving field with the potential to revolutionize care delivery. This study aimed to classify fertility rates and identify significant predictors using ML models among reproductive women in Ethiopia. This study utilized eight ML models in 5864 reproductive-age women using Ethiopian Demographic Health Survey (EDHS), 2019 data. Phyton programming language was used to develop these models. Predictors of fertility rate were determined using the feature important techniques. The performance of models was evaluated using accuracy, area under the curve (AUC), precision, recall, F1-score, specificity, and sensitivity. The mean age of participants was 32.7 (± 5.6) years. The random forest classifier (accuracy = 0.901 and AUC = 0.961) followed by a one-dimensional convolutional neural network (accuracy = 0.899 and AUC = 0.958), logistic regression (accuracy = 0.874 and AUC = 0.937), and gradient boost classifier (accuracy = 0.851 and AUC 0.927) were the top performing ML models. Family size, age, occupation, and education with an average importance score of 0.198, 0.151, 0.118, and 0.081, respectively were the top significant predictors of the fertility rate. The best ML models to classify and predict fertility rates were random forest, one-dimensional convolutional neural network, logistic regression, and gradient boost classifier. The findings on important factors of fertility rate can inform targeted public health, programs that address disparities related to family size, occupation, education, and other socioeconomic factors.

Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.

This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Guangxi Medical University, Yulin First People’s Hospital, and the MIMIC-IV database. The data from the Second Affiliated Hospital of Guangxi Medical University and the MIMIC-IV database were merged and randomly divided in a 7:3 ratio into a training set and an internal validation set, respectively. The dataset from Yulin First People’s Hospital was reserved as an external validation cohort. The primary study outcome was defined as in-hospital mortality.Feature selection was conducted using Lasso regression, followed by the development of six machine learning models: Logistic Regression, Random Forest (RF), Deep Neural Network (DNN), Support Vector Machine (SVM), LightGBM, and CatBoost. Model performance was assessed using the Area Under the Curve (AUC), accuracy, sensitivity, specificity, and F1 score. Model validation was performed through calibration and decision curve analysis. Feature importance was evaluated using SHAP, and subgroup analysis was conducted to assess the model’s applicability across different clinical scenarios. In internal validation, the Logistic Regression model performed the best, with an AUC of 0.86 (95% CI: 0.77–0.93), accuracy of 0.76, sensitivity of 0.73, specificity of 0.79, and an F1 score of 0.73. It outperformed other models (RF: AUC = 0.79, DNN: AUC = 0.78, SVM: AUC = 0.76, LightGBM: AUC = 0.71, CatBoost: AUC = 0.77). External validation yielded consistent results, with the Logistic Regression model’s AUC at 0.75 (95% CI: 0.56–0.92), accuracy of 0.69, sensitivity of 0.64, specificity of 0.73, and an F1 score of 0.66. Calibration curve analysis revealed that the Logistic Regression model had the lowest Brier score (0.1496), indicating the most reliable predicted probabilities. Decision curve analysis demonstrated that the model provided the highest net benefit across most decision thresholds. SHAP analysis identified lactate, age, and albumin as key predictors of mortality, with lactate and age positively correlated, and albumin negatively correlated. Subgroup analysis revealed better performance in the cardiac arrest group (AUC = 0.81), non-sepsis group (AUC = 0.75), and non-diabetes group (AUC = 0.78). The Logistic Regression-based mortality risk prediction model for V-A ECMO patients demonstrated comparable or even favorable performance to more complex machine learning models, with the advantage of higher interpretability.By explicitly incorporating lactate, age, and albumin as the principal predictors, this model facilitates precise risk stratification and provides practical support for clinical decision-making in ECMO management.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-25423-4.

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

Integrated STEM projects and experiments promote interdisciplinary learning, enhance critical thinking, and prepare students for real-world problem-solving by bridging science, technology, engineering, and mathematics. While numerous research studies have explored the STEM education domain, few have focused on supporting educators in identifying and designing effective integrated STEM projects and experiments. This research addresses this gap by introducing a novel system that leverages machine learning (ML) and large language models (LLMs) to assist educators in classifying and creating integrated STEM projects.We created and labeled a dataset of 1,071 project descriptions in this research work, mapped to one or more STEM areas (Science, Technology, Engineering, Mathematics). The dataset was developed using manually curated records, harvesting online sources, and synthetic data generation. Natural Language Processing (NLP) techniques were applied to prepare project descriptions for analysis. Various ML models, including Random Forest, Support Vector Machines (SVM), Logistic Regression, and Naive Bayes, were used for multi-label classification. The Random Forest model achieved the best performance, with a Precision of 91.99%, Recall of 94.59%, F1 Score of 93.27%, and Hamming Loss of 6.74%.The system operates in two phases: In Phase 1, the instructor proposes a project title with a description, and in response, the ML model identifies one or more STEM areas as the most relevant. In Phase 2, ChatGPT, specifically GPT-4o, assists educators by defining experiment objectives, generating ideas, designing procedures, and suggesting tools and resources. This human-in-the-loop system enhances collaboration between educators, ML models, and LLMs, streamlining the design of effective integrated STEM projects.This work's implications extend to improving the accessibility and effectiveness of integrated STEM education by reducing the barriers educators face in designing interdisciplinary projects. It provides a scalable framework for integrating advanced AI techniques into educational practices and fostering classroom innovation.

Digital imaging

Predicting traditional Chinese medicine constitutions in adults aged ≥ 65 years: A machine learning approach.

The current study aimed to develop and validate a machine learning (ML)-based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors. A cross-sectional study was conducted with 300 Saudi Arabian children (150 children with dyslexia, 150 controls) aged 6-12 years and their parents. Participants underwent assessments for attention, phonological awareness, rapid automatised naming (RAN), cognitive flexibility and other predictors. Four ML models-logistic regression, random forest, XGBoost and an ensemble-were trained and evaluated using performance metrics (AUC, sensitivity, specificity). Recursive feature elimination (RFE) identified key predictors. The RFE (15-fold cross-validation) identified attention, RAN, early language delay, phonological awareness and cognitive flexibility as the top five predictors of dyslexia. The ML models demonstrated high diagnostic accuracy for dyslexia detection. Logistic regression achieved superior performance with an area under the curve (AUC) of 0.95 (95% CI: 0.92-0.98), sensitivity of 97%, specificity of 91% and overall accuracy of 94%. Random forest and XGBoost yielded slightly lower but robust AUCs (0.91 and 0.93, respectively), with balanced sensitivity (95%) and specificity (91%). The ensemble model harmonised algorithmic strengths, retaining an AUC of 0.93 while prioritising interpretability through weighted contributions from XGBoost (40%), random forest (30%) and logistic regression (30%). This study demonstrated the transformative potential of ML in dyslexia diagnostics. By systematically prioritising phonological awareness, RAN and attention deficits, ML models offer a scalable, objective framework for early identification. These tools could alleviate reliance on subjective assessments, enabling timely interventions to mitigate dyslexia's long-term impacts.

Systematic Review of Machine Learning in Diagnosis of Myeloproliferative Neoplasia

BackgroundAccumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and severity of cardiovascular diseases (CVDs), resulting in increased healthcare demand. Effective prediction of demand for healthcare services, particularly those associated with peak events of CVDs, can be useful in optimizing the allocation of medical resources. However, few studies have attempted to adopt machine learning approaches with excellent predictive abilities to forecast the healthcare demand for CVDs. This study aims to develop and compare several machine learning models in predicting the peak demand days of CVDs admissions using the hospital admissions data, air quality data and meteorological data in Chengdu, China from 2015 to 2017.MethodsSix machine learning algorithms, including logistic regression (LR), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were applied to build the predictive models with a unique feature set. The area under a receiver operating characteristic curve (AUC), logarithmic loss function, accuracy, sensitivity, specificity, precision, and F1 score were used to evaluate the predictive performances of the six models.ResultsThe LightGBM model exhibited the highest AUC (0.940, 95% CI: 0.900–0.980), which was significantly higher than that of LR (0.842, 95% CI: 0.783–0.901), SVM (0.834, 95% CI: 0.774–0.894) and ANN (0.890, 95% CI: 0.836–0.944), but did not differ significantly from that of RF (0.926, 95% CI: 0.879–0.974) and XGBoost (0.930, 95% CI: 0.878–0.982). In addition, the LightGBM has the optimal logarithmic loss function (0.218), accuracy (91.3%), specificity (94.1%), precision (0.695), and F1 score (0.725). Feature importance identification indicated that the contribution rate of meteorological conditions and air pollutants for the prediction was 32 and 43%, respectively.ConclusionThis study suggests that ensemble learning models, especially the LightGBM model, can be used to effectively predict the peak events of CVDs admissions, and therefore could be a very useful decision-making tool for medical resource management.

Early Autism Spectrum Disorder (ASD) detection is important for early intervention.This study investigates the potential of eye-tracking (ET) data combined with machine learning (ML) models to classify ASD and Typically Developed (TD) children.Using a publicly available dataset, five ML models were evaluated: Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Random Forest improved with Convolutional Filters (ConvRF).The models were trained and tested using a set of evaluation metrics, including accuracy, precision, recall, F1-score, and ROC Area Under the Curve (AUC).Among these, the ConvRF model attained superior performance, achieving a recall of 90% and an AUC of 88%, indicating its robustness in identifying ASD children.These results highlight the model's effectiveness in ensuring high sensitivity, which is critical for early ASD detection.This study shows the promise of combining ML and eye-tracking technology as accessible non-invasive tools for enhancing early ASD detection, resulting in timely and personalized interventions.Limitations and recommendations for future research are also included.

This study applies machine learning (ML) techniques to classify fertile [for porphyry Cu and (or) Au systems] and barren adakites using geochemical data from New Brunswick, Canada. It emphasizes that not all intrusive units, including adakites, are inherently fertile and should not be directly used as the heat source evidence layer in mineral prospectivity mapping without prior analysis. Adakites play a crucial role in mineral exploration by helping distinguish between fertile and barren intrusive units, which significantly influence ore-forming processes. A dataset of 99 fertile and 66 barren adakites was analyzed using seven ML models: support vector machine (SVM), neural network, random forest (RF), decision tree, AdaBoost, gradient boosting, and logistic regression. These models were applied to classify 829 adakite samples from around the world into fertile and barren categories, with performance evaluated using area under the curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews correlation coefficient (MCC). SVM achieved the highest performance (AUC = 0.91), followed by gradient boosting (0.90) and RF (0.89). For model validation, 160 globally recognized fertile adakites were selected from the dataset based on well-documented fertility characteristics. Among the tested models, SVM demonstrated the highest classification accuracy (93.75%), underscoring its effectiveness in distinguishing fertile from barren adakites for mineral prospectivity mapping. Statistical analysis and feature selection identified middle rare earth elements (REEs), including Gd and Dy, with Hf, as key indicators of fertility. A comprehensive analysis of 1596 scatter plots, generated from 57 geochemical variables, was conducted using linear discriminant analysis (LDA) to determine the most effective variable pairs for distinguishing fertile and barren adakites. The most informative scatter plots featured element vs. element combinations (e.g., Ga vs. Dy, Ga vs. Gd, and Pr vs. Gd), followed by element vs. major oxide (e.g., Fe2O3T vs. Gd and Al2O3 vs. Hf) and ratio vs. element (e.g., La/Sm vs. Gd, Rb/Sr vs. Hf) plots, whereas major oxide vs. major oxide, ratio vs. ratio, and major oxide vs. ratio plots had limited discriminatory power.

ObjectiveTo build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).MethodsWe systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis.ResultsThe AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05).ConclusionThe prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.

To predict the gamma passing rate (GPR) in dosimetric verification of intensity-modulated radiotherapy (IMRT) using three machine learning models based on plan complexity and find the best prediction model by comparing and evaluating the prediction ability of the regression and classification models of three classical algorithms: artificial neural network (ANN), support vector machine (SVM) and random forest (RF). 269 clinical IMRT plans were chosen retrospectively and the GPRs of a total of 2340 fields by the 2%/2mm standard at the threshold of 10% were collected for dosimetric verification using electronic portal imaging device (EPID). Subsequently, the plan complexity feature values of each field were extracted and calculated, and a total of 6 machine learning models (classification and regression models for three algorithms) were trained to learn the relation between 21 plan complexity features and GPRs. Each model was optimized by tuning the hyperparameters and ten-fold cross validation. Finally, the GPRs predicted by the model were compared with measured values to verify the accuracy of the model, and the evaluation indicators were applied to evaluate each model to find the algorithm with the best prediction performance. The RF algorithm had the optimal prediction effect on GPR, and its mean absolute error (MAE) on the test set was 1.81%, root mean squared error (RMSE) was 2.14%, and correlation coefficient (CC) was 0.72; SVM was the second and ANN was the worst. Among the classification models, the RF algorithm also had the optimal prediction performance with the highest area under the curve (AUC) value of 0.80, specificity and sensitivity of 0.80 and 0.68 respectively, followed by SVM and the worst ANN. All the three classic algorithms, ANN, SVM, and RF, could realize the prediction and classification of GPR. The RF model based on plan complexity had the optimal prediction performance which could save valuable time for quality control workers to improve quality control efficiency.

Background/Objective: Subarachnoid hemorrhage (SAH) is associated with high morbidity and mortality rates, necessitating prognostic algorithms to guide decisions. Our study evaluates the use of machine learning (ML) models for predicting 1-month and 1-year mortality among SAH patients using national electronic health records (EHR) system. Methods: Retrospective cohort of 29,274 SAH patients, identified through national EHR system from January 2017 to December 2022, was analyzed, with mortality data obtained from central civil registration system in Türkiye. Variables included (n = 102) pre- (n = 65) and post-admission (n = 37) data, such as patient demographics, clinical presentation, comorbidities, laboratory results, and complications. We employed logistic regression (LR), decision trees (DTs), random forests (RFs), and artificial neural networks (ANN). Model performance was evaluated using area under the curve (AUC), average precision, and accuracy. Feature significance analysis was conducted using LR. Results: The average age was 56.23 ± 16.45 years (47.8% female). The overall mortality rate was 22.8% at 1 month and 33.3% at 1 year. One-month mortality increased from 20.9% to 24.57% (p < 0.001), and 1-year mortality rose from 30.85% to 35.55% (p < 0.001) in the post-COVID period compared to the pre-COVID period. For 1-month mortality prediction, the ANN, LR, RF, and DT models achieved AUCs of 0.946, 0.942, 0.931, and 0.916, with accuracies of 0.905, 0.901, 0.893, and 0.885, respectively. For 1-year mortality, the AUCs were 0.941, 0.927, 0.926, and 0.907, with accuracies of 0.884, 0.875, 0.861, and 0.851, respectively. Key predictors of mortality included age, cardiopulmonary arrest, abnormal laboratory results (such as abnormal glucose and lactate levels) at presentation, and pre-existing comorbidities. Incorporating post-admission features (n = 37) alongside pre-admission features (n = 65) improved model performance for both 1-month and 1-year mortality predictions, with average AUC improvements of 0.093 ± 0.011 and 0.089 ± 0.012, respectively. Conclusions: Our study demonstrates the effectiveness of ML models in predicting mortality in SAH patients using big data. LR models’ robustness, interpretability, and feature significance analysis validate its importance. Including post-admission data significantly improved all models’ performances. Our results demonstrate the utility of big data analytics in population-level health outcomes studies.