Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation

BackgroundCardiovascular disease is the leading cause of morbidity and mortality in kidney transplant recipients (KTRs). Conventional risk assessment tools often underperform in this population due to their unique cardiovascular milieu shaped by end-stage kidney disease and immunosuppression.ObjectivesThis study aimed to evaluate the performance of machine learning (ML) models in predicting major adverse cardiovascular events in KTRs.MethodsWe analyzed a data set of 518 adult KTRs from a single center (2015-2024) incorporating 1,014 demographic, clinical, laboratory, and imaging parameters in addition to ischemic work-up and revascularization details. After preprocessing, 44 features were used to train and validate ML models using AutoGluon with 5-fold cross-validation and permutation-based feature selection as well as domain knowledge. SHAP was used for model interpretation. The best performing ML model was compared to multivariable logistic regression in addition to a historical model from prior literature, that is, Soveri risk score.ResultsOver a mean follow-up of 5.3 ± 2.3 years, 102 patients experienced major adverse cardiovascular events (mean age 52.9 ± 13.2 years; 56.9% male). The CatBoost model achieved the highest area under the receiver-operating characteristic curve (AUROC) (0.72; 95% CI: 0.607-0.811) and area under the precision-recall curve (0.36; 95% CI: 0.230-0.540), greater than the logistic regression (AUROC = 0.67; 95% CI: 0.559-0.772, area under the precision-recall curve = 0.31; 95% CI: 0.228-0.553). The differences were not statistically significant. Both models had significantly higher AUROC than the Soveri risk score. Top predictive variables included coronary artery disease, left atrial reservoir strain, dialysis duration, and left ventricular end-diastolic volume index.ConclusionsML models are feasible for predicting cardiovascular events in KTRs.

The role of continuous monitoring in acute-care settings for predicting all-cause 30-day hospital readmission: A pilot study.

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. The performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.

Accurate wind speed forecasting is essential for daily activities and socio‐economic production. This study employs an explainable machine learning (ML) model to correct wind speed forecasts from a numerical weather prediction (NWP) model over weather stations across lead times of 1–24 hr in Zhejiang Province, China. Eleven meteorological variables derived from the NWP model are used as predictors. The results demonstrate that ML model substantially improves the original NWP forecasts, reducing the root mean square error of approximately 20%–40% at most stations and about 10% at certain coastal stations. The corrected forecasts also consistently achieve higher Kling‐Gupta Efficiency scores and Forecast Accuracy across all lead times. To enhance model interpretability, we apply Split Count and SHapley Additive exPlanation analyses. The results indicate that the friction velocity (UST) is the most influential predictor and reveal a threshold beyond which the ML model's correction performance improves markedly. The ML model performance slightly declines in coastal regions, likely due to the inconsistent representation of UST between the land and water surface in the NWP model. Monthly analysis further shows superior model performance in November and December, when UST values are higher. This study underscores the value of explainable ML models in improving NWP wind forecasts and provides insights for correcting meteorological variables in NWP models.

Objective: To investigate the feasibility of a machine learning (ML) model based on radiomic features to identify active giant cell arteritis (GCA) in the aorta and differentiate it from atherosclerosis in follow-up [18F]FDG-PET/CT images for therapy monitoring. Methods: To train the ML model, 64 [18F]FDG-PET scans of 34 patients with proven GCA and 34 control subjects with type 2 diabetes mellitus were retrospectively included. The aorta was delineated into the ascending, arch, descending, and abdominal aorta. From each segment, 95 features were extracted. All segments were randomly split into a training/validation (n = 192; 80%) and test set (n = 46; 20%). In total, 441 ML models were trained, using combinations of seven feature selection methods, seven classifiers, and nine different numbers of features. The performance was assessed by area under the curve (AUC). The best performing ML model was compared to the clinical report of nuclear medicine physicians in 19 follow-up scans (7 active GCA, 12 inactive GCA). For explainability, an occlusion map was created to illustrate the important regions of the aorta for the decision of the ML model. Results: The ten-feature model with ANOVA as the feature selector and random forest classifier demonstrated the highest performance (AUC = 0.92 ± 0.01). Compared with the clinical report, this model showed a higher PPV (0.83 vs. 0.80), NPV (0.85 vs. 0.79), and accuracy (0.84 vs. 0.79) in the detection of active GCA in follow-up scans. Conclusions: The current radiomics ML model was able to identify active GCA and differentiate GCA from atherosclerosis in follow-up [18F]FDG-PET/CT scans. This demonstrates the potential of the ML model as a monitoring tool in challenging [18F]FDG-PET scans of GCA patients.

Optimizing asphalt mix design through predicting effective asphalt content and absorbed asphalt content using machine learning

12074 Background: Skeletal-related events (SREs) are detrimental clinical events in bone metastasis from advanced cancer, defined by pathologic fracture, spinal cord compression, and inevitable surgical or radiational intervention to the bone. Given their negative impact on quality of life and prognosis and interpatient heterogeneity in the SRE risk, accurate identification of patients with high SRE risk is critical. Methods: The patient-level data from three randomized clinical trials that administered zoledronic acid (ZA) to patients with bone-metastatic breast cancer, castration-resistant prostate cancer (CRPC), and other types of cancer were analyzed. (N = 460, 452, and 315, respectively) Machine learning (ML) models to predict SREs within 18 months (breast cancer), 12 months (CRPC), and 9 months (other cancers) were developed based on more than 40 baseline clinical and laboratory data. Seven ML algorithms and five feature selection methods were utilized to develop multiple models. The ML model with the best performance was identified based on the F1 score and the area under the receiver operating characteristic curve (AUC-ROC) for each cancer type and interrogated for important features with Shapley additive explanations. Lastly, the ML models’ ability to stratify patients by the cumulative SRE risk was evaluated by calculating hazard ratio (HR) with Cox-proportional hazards models. Results: Among the multiple ML models developed with different algorithms and feature selection methods, the model developed utilizing the random forests algorithm and the Boruta method for selecting features demonstrated the best performance in all types of cancer (F1 0.70, 0.67, and 0.67, and AUC-ROC 0.72, 0.68, and 0.73 for breast cancer, CRPC, and other cancers, respectively). In the ML model for breast cancer, performance status (PS), history of SRE, serum alkaline phosphatase (ALP), history of anti-neoplastic surgery, radiation therapy, and pathologic fracture were included as important features. Serum ALP, albumin, sodium, Gleason scores, and geographic regions were shown to be relevant in the CRPC model. For other cancers, serum ALP, albumin, total protein, phosphorus, red blood cell count, white blood cell count, visceral metastases, and history of arthritis were incorporated in the ML model. The ML model prediction successfully stratified the patients for cumulative SRE risk in all three cohorts. (HR with 95% confidence interval: 2.43 [1.86 – 3.18], 1.92 [1.51 – 2.45], and 3.06 [2.29 – 4.09] for breast cancer, CRPC, and other types of cancer, respectively). Conclusions: ML models incorporating baseline clinical and laboratory data can identify patients with bone metastasis on ZA harboring a high SRE risk.

MP07-06 EVALUATING THE EFFICACY OF ARTIFICIAL INTELLIGENCE IN PREDICTING OUTCOMES FOR PEDIATRIC HYDRONEPHROSIS: A SYSTEMATIC REVIEW

Background: Osteoporosis is a systemic skeletal disorder marked by reduced bone density and microarchitectural deterioration, leading to increased fracture risk. While the dual-energy X-ray absorptiometry (DEXA) scan is the World Health Organization (WHO)-recommended diagnostic standard, its limitations necessitate alternative methods. Emerging magnetic resonance imaging (MRI) techniques, radiomics, and machine learning promise to enhance osteoporosis diagnosis through detailed analysis of lumbar MRI apparent diffusion coefficient (ADC) maps, potentially revolutionizing early detection and treatment strategies. Objectives: In this study, we are going to evaluate the performance of machine learning (ML) models using radiomics features of lumbar MRI ADC map for osteoporosis detection, and to identify significant features and their diagnostic thresholds. Specific performance metrics such as accuracy, sensitivity, specificity, and Area Under the receiver operating characteristic (ROC) Curve (AUC) were assessed. Patients and Methods: This retrospective study employed a cross-sectional design, with a total of 140 cases, including 21 with osteoporosis. The study's inclusion criteria consisted of concurrent lumbar MRI and DEXA within a year, while exclusion criteria included infectious or neoplastic lumbar lesions, fractures, instrumentation, significant osteodegenerative changes, cases where the first four lumbar vertebrae were not included in the imaging field, and absence of diffusion-weighted imaging. Manual segmentation of lumbar vertebrae from ADC maps was performed to create a comprehensive dataset, comprising 5,580 radiomics features per case. Subsequently, the top five features selected by fast correlation-based filter (FCBF) were used to test the performance of seven Machine Learning algorithms (k-Nearest neighbors, decision tree, random forest, logistic regression, support vector machine, naive bayes, and neural network). Statistical tests and ROC curve analysis were conducted to determine the significance and thresholds of these features. Results: The study included 140 cases, with 132 females (94.3%) and 8 males (5.7%), and a mean age of 65.32 ± 8.50 years. The mean BMI was 31.43 ± 5.53 kg/m² for females and 26 ± 3.59 kg/m² for males. In terms of demographic differences, no significant age difference was found between the osteoporotic and non-osteoporotic groups (P = 0.889). However, the osteoporotic group had significantly lower mean body weight (64.90 ± 10.13 kg vs. 74.68 ± 13.94 kg, P = 0.003) and BMI (27.40 ± 4.38 kg/m² vs. 31.77 ± 5.52 kg/m², P = 0.001) compared to the non-osteoporotic group. The median interval between DEXA and lumbar MRI was 1 month (range 0.1 - 11.87 months). The Neural Network model demonstrated the highest performance with an AUC of 0.616 and a classification accuracy of 0.764 using all features. The Naive Bayes model, using the top five features selected by FCBF, showed the highest performance with an AUC of 0.913, accuracy of 0.907, sensitivity of 0.667, and specificity of 0.95. All ML models’ performance were elevated by feature selection. Independent t-tests and Mann-Whitney U tests identified 521 and 670 significant features, respectively (P < 0.05). ROC analysis revealed 58 features with AUC values above 0.70. Conclusion: This study's findings suggest that ML models, particularly the Naive Bayes algorithm, can effectively use lumbar ADC map radiomics to diagnose osteoporosis. These findings could enhance early detection and treatment strategies, potentially improving patient outcomes and reducing the burden of osteoporotic fractures. This study also established threshold values for significant features.

Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models. This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021. Using public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images. The analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis. For the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively. These findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models.

Background Peripheral Arterial Disease(PAD) is a prevalent cardiovascular condition affecting millions worldwide, and the advent of machine learning(ML) techniques offers promising opportunities to enhance risk prediction and personalise patient management in this complex disease. This systematic review and meta-analysis aimed to evaluate the performance of ML models in predicting adverse outcomes in PAD patients compared to traditional statistical approaches. Methods A comprehensive search of major databases(MEDLINE,EMBASE,CINAHL,Cochrane CENTRAL) was conducted for studies published between 2000 and 2024. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the PROBAST tool. The quality of included studies was also assessed using criteria outlined by Qiao et al. A modified Hierarchical Summary Receiver Operating Characteristic(HSROC) analysis was performed to compare the predictive performance of various ML models and traditional regression methods. Results Thirteen studies met the inclusion criteria. Gradient boosted models demonstrated the highest predictive performance with a diagnostic odds ratio(DOR) of 36.593(95% CI:24.44-54.79), sensitivity of 0.853, and specificity of 0.863. The best performing ML models for outcomes within 2 years, showed a DOR of 34.169(95% CI:22.840-51.117). Traditional regression models consistently underperformed compared to ML approaches, with the lowest DOR of 3.326(95% CI:1.814-6.097). The quality assessment revealed a mix of methodological rigor, with 54% of studies rated as low risk of bias and 46% as unclear. Conclusion Advanced ML techniques demonstrate superior predictive power for adverse outcomes in PAD patients compared to traditional regression methods.

Machine Learning (ML) models have been extensively applied in various fields to enhance prediction. For instance, in cybersecurity, they examine large amounts of data, establish trends in the data, and draw insights from previous events, to enhance detection and respond to cyber threats. Random Forest, Logistic Regression, K-Nearest Neighbor and LSTM are some of the popular ML models vastly used for anomaly detection. The accuracy of these models is therefore the cornerstone of the organization’s information systems security since wrong predictions result to false positives and negatives which significantly reduce employees’ output and may result into workers’ frustrations when interacting with the information systems. Among the many factors that affect ML model performance, data pre-processing has been underscored. Using the various publicly available datasets, this paper examines the impact of data preprocessing techniques on selected ML model architectures’ performance. Training time, Accuracy, Precision, Recall and F1 scores are used for evaluating the ML models’ performance.

Background: Coronary Artery Disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations, resulting in unnecessary preventive treatment and reduced cost-effectiveness and safety. Combining machine learning model with the metaheuristic optimisation approach using the PSO algorithm may outperform QRISK3 in predicting CAD. It improves performance by selecting the best-performing subset of features related to clinical outcomes. Research Question: Does the performance of Machine Learning Models Combined with PSO Algorithm for Feature Selection as a Metaheuristic Optimisation Approach in Predicting Coronary Arterial Disease using the UK Biobank dataset outperform the QRISK3 calculator? Aims: This study is to assess the accuracy of QRISK3 in predicting CAD using the UK Biobank dataset. It aims to evaluate the efficacy of machine learning models on the identical dataset for predicting CAD. The work utilises the PSO algorithm for feature selection to identify the optimal subset of features from the UK Biobank dataset. Methods: This study utilises data from the UK Biobank. The dataset consists of 348,015 participants aged 24-84 with no prior diagnosis of CAD. The performance of both QRISK3 and machine learning models was evaluated separately using ROC analysis. Several machine learning models were employed: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Gradient Boosting. The dataset was split into training and test sets with a ratio of 4:1 for the machine learning models. Each model has been developed by adding a PSO algorithm to enhance the model's classification accuracy. Results: Out of the total 348,015 participants, 23,136 individuals (6.64%) were diagnosed with CAD within 10 years following their first visit, while 324,879 individuals (93.4%) did not develop CAD. The AUC value of the QRISK3 prediction is 0.6113, while the combined machine learning models using the PSO algorithm using the gradient boosting model achieve an AUC of 0.7258, showing better performance. Conclusions: This study shows hybrid machine learning models optimised with the PSO algorithm can better predict CAD than QRISK3. These ML models can effectively identify high-risk CAD patients, allowing for more personalised preventative strategies and supporting policymakers in implementing lifestyle change recommendations.