8.473 hasil untuk machine learning
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Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database
…patients with immunoglobulin A nephropathy from billing data using machine learning. The medical records and bills of 3,743 patients who consulted nephrologists at a single center were extracted. Patients were labeled to have been diagnosed with immunoglobulin A nephropathy through a review of…
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PHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning
…consisting of mobile devices, edge servers, and cloud servers. Automated machine learning is used to identify the optimal classification models, along with tuning their hyper-parameters, which results in adaptive boosting ensemble learning-based models to reduce the time complexity of the system to…
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Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments
…the ability to automatically detect changes in brain activity using machine learning and complex networks. Machine learning was applied at three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of the EEG time series, and (C) the complex network measures…
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Predicting arthritis risk with machine learning: Insights from the 2023 National Health Interview Survey data
…test. Fourteen key predictors were screened using support vector machine recursive feature elimination (SVM-RFE): age, general health, chronic obstructive pulmonary disease, gender, hypertension, coronary heart disease, body mass index (BMI), cancer, depression, dementia, asthma, diabetes, smoking…
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Machine learning meets maternal health: Uncovering spatial blind spots in antenatal care quality in Bangladesh
…quality of ANC. Using nationally representative data, this paper applies machine learning (ML) approaches to classify ANC quality, identify regional hotspots of low-quality care, and its factors. Methods: This study used data from the 2022 Bangladesh Demographic and Health Survey (BDHS), with a…
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Comment on “Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper”
A recent study by Faye Orcales and colleagues proposes a teaching curriculum on supervised machine learning applied to genomics data aimed at predicting antibiotic resistance. The article describes a traditional machine learning pipeline step-by-step in a way that is accessible to anyone, including…
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Machine learning identifies pupil size and corneal thickness as key predictors of axial elongation rate
Purpose: This study aimed to develop a machine learning-based prediction model for myopia progression using ocular biometric parameters to provide an objective assessment tool for clinical practice. Methods: A retrospective analysis was conducted on patients treated at Shanghai Parkway Health…
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Telemedicine adoption in cardiology: Determinants and predictors identified using Bayesian Model Averaging and Machine Learning
…We applied Bayesian Model Averaging (BMA) for explanatory analysis and Machine Learning (ML) for predictive modeling. BMA identified TM determinants after excluding collinear variables and selecting variables based on LASSO regression. The extreme gradient boosting (XGBoost) ML algorithm predicted…
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upsML: A high-accuracy machine learning classifier for predicting Plasmodium falciparum var gene upstream groups
…such as the DBLα tag or RNA-Seq assemblies. We developed upsML, a machine-learning-based classifier trained on 2,530 curated var genes, to accurately assign upstream groups based on sequence features from different partial gene regions. We compared seven methods, including support vector machines…
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Identifying factors associated with vaping cessation in young adults: A machine learning and XAI approach
…behaviors and attitudes among young adult vapers and evaluate machine learning and explainable AI methods for predicting quit attempts and cessation success. A social media–based survey captured behavioral, contextual, and demographic factors, and cessation was defined as self‑reported abstinence…
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Clustering of disease trajectories with explainable machine learning: A case study on postoperative delirium phenotypes
…In this paper, we propose an approach that combines supervised machine learning for personalized POD risk prediction with unsupervised clustering technique to uncover potential POD phenotypes. We first demonstrate our approach using synthetic data, where we simulate patient cohorts with predefined…
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Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial
…requiring an ambulance than there are available ambulances are common. Machine Learning (ML) techniques offer a promising but largely untested approach to assessing relative risks among these patients. The study aims to establish whether the provision of ML-based risk scores predicting patient…
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Comparison of machine learning methods in forecasting and characterizing the birch and grass pollen season
…speed, mean pressure at sea level, global radiation and snow depth. Eight machine learning models from four distinct families (lazy, linear, tree-based, and deep learning) were chosen to estimate the probability of the occurrence of pollen concentration within specified categories. These…
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National data meets AI: Machine learning for predicting overweight/obesity among ever-married Bangladeshi women
…identifying risk factors and predicting overweight and obesity through machine learning (ML) classifiers among ever-married Bangladeshi women aged 15–49 years. Data from the 2017–2018 BDHS, a nationally representative survey, were examined. The data were pre-processed and subsequently balanced…
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Sentiment analysis of cancer screening in Chinese social media: Qualitative studies based on machine learning
Purpose: Explore public perceptions and sentiments about cancer screening on social media. The dissemination of misinformation and negative attitudes continue to impede the access of many individuals with perceived risk to cancer screening services despite their awareness of the necessity and…
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A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer
…molecular cargo to achieve intercellular communication. Herein, a novel machine learning strategy was reported for rapid, efficient screening of biomarkers and identified an optimal exosome RNA combination as diagnostic biomarker of SCLC. Firstly, RNA sequencing data from 111 SCLC patients and 362…
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Machine learning-based prediction of diabetic retinopathy from pupillary abnormalities in a South Indian population
…Velocity of Pupillary Dilatation (VPD) were collected and analyzed using machine learning algorithm including Support Vector Machine, Decision Trees, Artificial Neural Networks (ANN), Logistic Regressions, Random Forest, Naive Bayes Classifier. Utilizing ROC analysis and the Youden index, this…
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Insights into long-acting reversible contraceptive practices in Sub-Saharan Africa: A machine learning perspective
…the need for data-driven insights to address this gap. This study applies machine learning (ML) to identify key predictors of LARC use and guide interventions. Methods: Nationally representative data from 14,275 women across nine SSA countries were analyzed. Preprocessing included k-NN imputation…
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A hybrid machine learning model for pulmonary tuberculosis forecasting of Chongqing with adjacent-region data
…regression (SVR) model in parallel, followed in series by an extreme learning machine (ELM) optimized via the sparrow search algorithm. Furthermore, recognizing the notable spatial correlation characteristic of airborne PTB transmission, this study incorporates PTB incidence data from surrounding…
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