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Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine
BackgroundQuantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To...
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The accuracy of an Online Sequential Extreme Learning Machine in detecting voice pathology using the Malaysian Voice Pathology Database
BackgroundA multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the...
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Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model
Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to...
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First experiences with machine learning predictions of accelerated declining eGFR slope of living kidney donors 3 years after donation
BackgroundLiving kidney donors are screened pre-donation to estimate the risk of end-stage kidney disease (ESKD). We evaluate Machine Learning (ML)...
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Machine learning-based approach for predicting low birth weight
BackgroundLow birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health...
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Machine learning based on SEER database to predict distant metastasis of thyroid cancer
ObjectiveDistant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant...
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Machine learning models for predicting preeclampsia: a systematic review
BackgroundThis systematic review provides an overview of machine learning (ML) approaches for predicting preeclampsia.
MethodThis review adhered to...
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Integrated machine learning and deep learning for predicting diabetic nephropathy model construction, validation, and interpretability
ObjectiveTo construct a risk prediction model for assisted diagnosis of Diabetic Nephropathy (DN) using machine learning algorithms, and to validate...
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Machine learning algorithms for predicting COVID-19 mortality in Ethiopia
BackgroundCoronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily...
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Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy
ObjectiveThe objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of...
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Individualized estimation of arterial carbon dioxide partial pressure using machine learning in children receiving mechanical ventilation
BackgroundMeasuring arterial partial pressure of carbon dioxide (PaCO 2 ) is crucial for proper mechanical ventilation, but the current sampling...
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Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis
Background and objectivesSepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended...
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Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis
BackgroundSepsis is a life-threatening condition caused by an abnormal response of the body to infection and imposes a significant health and...
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A systematic review of machine learning models for management, prediction and classification of ARDS
AimAcute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral...
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Interpretable prediction of 3-year all-cause mortality in patients with chronic heart failure based on machine learning
BackgroundThe goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that...
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Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study
BackgroundThe goal is to use three different machine learning models to predict the recurrence of breast cancer across a very heterogeneous sample of...
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Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics
PurposeThis study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive...
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Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers
BackgroundThis study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory...
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Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review
IntroductionThis systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.
MethodsThe PRISMA...
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Machine learning-based risk models for procedural complications of radiofrequency ablation for atrial fibrillation
BackgroundRadiofrequency ablation (RFA) for atrial fibrillation (AF) is associated with a risk of complications. This study aimed to develop and...
