8.473 hasil untuk machine learning
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Integrative multi-omics and network-based machine learning for early diagnosis of Parkinson’s disease
Background: Accurate diagnosis of Parkinson’s Disease (PD) remains challenging due to its biological complexity. Integrating machine learning with multi-omics and network topological analyses may enhance diagnostic precision. Objective: To classify early-stage PD patients and healthy controls (HCs)…
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Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses
Chronic kidney disease (CKD) is a progressive condition requiring early detection for optimal patient outcomes. This study developed an interpretable machine learning framework using XGBoost with SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for…
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Expression of Concern: A practical approach for colorectal cancer diagnosis based on machine learning
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Integrating machine learning with SHAP to uncover multi-tissue molecular signatures in Osteoarthritis progression
…diagnostic biomarkers using an integrative framework combining multiple machine learning (ML) algorithms and SHapley Additive exPlanations (SHAP). Gene expression profiles from cartilage, synovium, and peripheral blood were retrieved from the GEO database. DEGs were identified across tissues…
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Beyond health: A machine learning analysis of structural barriers to school attainment in Somalia
In fragile states like Somalia, the link between poor health and educational exclusion is critical yet poorly understood. This study uses a novel machine learning approach to identify and rank the most significant barriers to school attendance. We analyzed nationally representative data from 10,511…
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Clinically interpretable electrovectorcardiographic machine learning criteria for the detection of echocardiographic left ventricular hypertrophy
…ECG with vectorcardiography (VCG) using a clinically interpretable machine learning algorithm (C5.0) could improve diagnostic performance. We analyzed ECG and VCG data from 664 patients, 42.8% of whom had Echo-LVH. The study introduced three new criteria—Marcos VCG, Marcos VCG-ECG, and Marcos…
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Fall prediction in a quiet standing balance test via machine learning: Is it possible?
…higher risk of falling. An innovative approach for fall prediction is the machine learning. Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. Thus, to assess the performance of machine learning algorithms…
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Refining the rheological characteristics of high drug loading ointment via SDS and machine learning
…factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified…
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Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review
Objective: This review aimed to summarize the use of machine learning for predicting the potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of predictive models, and to provide recommendations for future models. Materials and methods: This systematic review was…
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Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning
…with a weight of 39%, 21%, and 14%, respectively. We also proposed a machine learning model that uses Extreme Gradient Boosting (XGBoost) to detect lung cancer severity levels in lung cancer patients. We used a dataset of 1000 lung cancer patients and 465 individuals free from lung cancer from…
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How to understand high global food price? Using SHAP to interpret machine learning algorithm
…analysis, this study uses monthly data from January 2000 to May 2022 and machine learning models to examine the root causes of that period’s global food price surge and global food security situation. The results show that: Firstly, the increase in the supply of US dollars and the rise in oil…
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Machine learning algorithms can predict tail biting outbreaks in pigs using feeding behaviour records
…the occurrence of the tail biting event. This study aimed to build a machine-learning algorithm for real-time detection of upcoming tail biting outbreaks, using feeding behaviour data recorded by an electronic feeder. Prediction capacities of seven machine learning algorithms (Generalized Linear…
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Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning
…it. To this end, predictive analysis is performed using public data and machine learning techniques. Methods and materials: This study used data from the ‘2020 Report on Digital Information Divide Survey’ published by the Korea National Information Society Agency. In establishing the prediction…
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Questionnaire-free machine-learning method to predict depressive symptoms among community-dwelling older adults
…This study aimed to develop and validate a questionnaire-free, machine-learning model as an alternative triage test for the GDS-15 among community-dwelling older adults. The best models were the random forest (RF) and deep-insight visible neural network by internal validation, but both performances…
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An efficient machine learning framework to identify important clinical features associated with pulmonary embolism
A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as…
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Exploration and demonstration of explainable machine learning models in prosthetic rehabilitation-based gait analysis
Quantitative gait analysis is important for understanding the non-typical walking patterns associated with mobility impairments. Conventional linear statistical methods and machine learning (ML) models are commonly used to assess gait performance and related changes in the gait parameters…
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Arterial stiffness and biological parameters: A decision tree machine learning application in hypertensive participants
Arterial stiffness, measured by arterial stiffness index (ASI), could be considered a main denominator in target organ damage among hypertensive subjects. Currently, no reported ASI normal references have been reported. The index of arterial stiffness is evaluated by calculation of a stiffness…
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Interpretable machine learning models to predict short-term postoperative outcomes following posterior cervical fusion
…plans that reduce the likelihood of unfavorable outcomes. We developed machine learning models for predicting short-term postoperative outcomes and incorporate these models into an open-source web application in this study. The American College of Surgeons National Surgical Quality Improvement…
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Calibration of pedestrian ingress model based on CCTV surveillance data using machine learning methods
Machine learning methods and agent-based models enable the optimization of the operation of high-capacity facilities. In this paper, we propose a method for automatically extracting and cleaning pedestrian traffic detector data for subsequent calibration of the ingress pedestrian model. The data…
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