This study used an unsupervised machine learning algorithm, sidClustering and random forests, to identify clusters of risk behaviors of Bacterial Vaginosis (BV), the most common cause of abnormal vagi...
We identified four clusters, and variables were ranked by importance in distinguishing clusters: Cluster 1: nulliparous women who engaged in IVPs to clean themselves and please sexual partners, and us...
Machine learning methods may be particularly useful in identifying specific clusters of high-risk behaviors, in developing interventions intended to reduce BV and IVP, and ultimately in reducing the r...
Changes in error processing are observable in a range of anxiety-related disorders. Numerous studies, however, have reported contradictory and nonreplicating findings, thus the exact mapping of brain ...
Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly unde...
To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samp...
Our findings revealed significantly elevated G6PD expression levels in liver cancer tissues as compared to normal tissues. Meanwhile, Nomogram and Adaboost, Catboost, and Gbdt Regression analyses show...
The potential diagnostic utility of G6PD and Decision Tree C5.0 for LIHC opens up a novel avenue for early detection and improved treatment strategies for hepatocellular carcinoma....
As autonomous vehicles (AVs) advance from theory into practice, their safety and operational impacts are being more closely studied. This study aims to contribute to the ever-evolving algorithms used ...
Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various pr...
Using the National Trauma Data Bank from 2013 to 2018, we included severely injured trauma patients and extracted clinical features available from the pre-hospital and emergency department. We subsequ...
A total of 326,758 patients met our inclusion with 18,871 (5.8%) requiring massive transfusion. Emergency department models demonstrated strong performance characteristics with mean areas under the re...
We demonstrate the use of machine learning in developing an accurate prediction model for massive transfusion in trauma patients using early clinical data. This research demonstrates the potential uti...
Esophageal cancer (EC) is a highly prevalent and progressive disease. Early prediction of EC risk in the population is crucial in preventing this disease and enhancing the overall health of individual...
The current retrospective study was performed from 2018 to 2022 in Sari City based on 3256 EC and non-EC cases. The six selected algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XG...
Comparing the performance efficiency of algorithms revealed that the XG-Boost model gained the best predictability for EC risk with AU-ROC = 0.92 and AU-ROC-test = 0.889 for internal and validation st...
This study showed that the XG-Boost could provide insight into the early prediction of the EC risk for people and clinical providers to stratify the high-risk group of EC and achieve preventive measur...
Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disor...
Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation ...
Postpancreatectomy hemorrhage (PPH) is a rare yet dreaded complication following pancreaticoduodenectomy (PD). This retrospective study aimed to explore a machine learning (ML) model for predicting PP...
A total of 284 patients who underwent open PD at our institute were included in the analysis. To address the issue of imbalanced data, the adaptive synthetic sampling (ADASYN) technique was employed. ...
PPH occurred in 11 patients (3.9%), with a median onset time of 22 days postoperatively. These minority cases were oversampled to 85 using ADASYN. The extra trees classifier demonstrated superior perf...
This study highlights the potential of the ML approach to predict PPH occurrence following PD. Vigilance and early interventions guided by such model predictions could positively impact outcomes for h...
No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as over...
In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice d...
From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to ...
This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying...
