8.473 hasil untuk machine learning
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Predicting depression among men who have sex with men in Ghana using machine learning algorithms
…and undertreated in this population. This study applied seven tree-based machine learning (ML) models using tree-based classifiers: Decision Tree, Random Forest, Gradient Boosting, AdaBoost, XGBoost, LightGBM, and CatBoost to identify key psychosocial predictors of depression in a sample of 225 MSM…
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Trustworthy AI for medical decisions: Adversarially robust and fair machine learning prediction for Parkinson’s disease
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, including tremor, rigidity, and postural instability. Machine learning (ML) models have shown promise for the diagnosis of PD; however, many existing approaches do not explicitly address fairness…
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Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping
…a randomised clinical trial to explore how gradient boosting (XGBoost) machine learning compares with traditional analysis when ‘ground truth’ treatment responsiveness depends on the interaction of multiple phenotypic variables. As expected, traditional analysis detected a significant treatment…
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Cardiovascular disease detection: A hybrid machine learning-AI framework for personalized diagnosis and risk assessment
…computational framework that synergistically integrates a Support Vector Machine (SVM) classifier, a Particle Swarm Optimization (PSO) algorithm for hyperparameter tuning, and an AI-based interpretation module (SHapley Additive exPlanations, SHAP) to enable early diagnosis and risk assessment…
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Screening mild cognitive impairment using aspects of personal, social, and functional lifestyle: Machine Learning Approaches
…many patients in primary care go undetected. This study examines whether machine learning (ML) models can predict MCI using routinely collected personal, social, and functional lifestyle factors and identifies the most important predictors. Methods: Data from round 2 and 3 of the National Social…
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Correction: A machine learning approach for estimating forage maize yield and quality in NW Spain
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Artikel Jurnal Teks lengkap
Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning
…further decline. We developed and evaluated lightweight, explainable machine learning models to forecast adverse physiological events up to 15 minutes in advance using continuously streamed vital signs and clinical data. Models were trained and evaluated on 1,519 transports conducted by a…
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Coral restoration alters reef soundscapes but machine learning and manual analyses suggest different recovery rates
…of soundscape analysis: manual detection of unidentified fish sounds; and machine learning-based Uniform Manifold Approximation and Projection analysis. Results were approach-specific: the manual approach highlighted similarities in fish calls between the restoration site and the healthy reference…
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Applicability of machine learning technique in the screening of patients with mild traumatic brain injury
…intervention. As such, this study aims to evaluate the applicability of a machine learning (ML) technique in the screening of patients with mild TBI in the Regional University Hospital of Maringá, Paraná state, Brazil. This is an observational, descriptive, cross-sectional, and retrospective study…
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Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia
…system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia. Materials and methods: The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed…
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Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning
…by PROTACs. In this study, we have developed PrePROTAC, an interpretable machine learning model based on a transformer-based protein sequence descriptor and random forest classification. PrePROTAC predicts genome-wide targets that can be degraded by CRBN, one of the E3 ligases. In the benchmark…
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Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality
…especially in those who are at high risk. Thus, we evaluated whether a machine-learning system can predict accurately these risks in CKD patients and attempted its application by developing a Web-based risk-prediction system. We developed 16 risk-prediction machine-learning models using Random…
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Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques
Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive…
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A novel machine learning-based approach for the detection and analysis of spontaneous synaptic currents
…rely on classical threshold methods, while the third represents a novel machine learning-based algorithm. Comparison with frequently used existing methods demonstrates the suitability of our algorithms for an unbiased and efficient analysis of synaptic signals in the central nervous system.
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A machine learning approach to analyse and predict the electric cars scenario: The Italian case
…distribution network. In this work we proposed a novel application of machine learning methods for the estimation of factors which could impact the distribution of the circulating fleet of electric cars in Italy. We first collected a new dataset from public repository to evaluate the most relevant…
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Training machine learning algorithms for automatic facial coding: The role of emotional facial expressions’ prototypicality
Automatic facial coding (AFC) is a promising new research tool to efficiently analyze emotional facial expressions. AFC is based on machine learning procedures to infer emotion categorization from facial movements (i.e., Action Units). State-of-the-art AFC accurately classifies intense and…
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Machine learning based classification of mitochondrial morphologies from fluorescence microscopy images of Toxoplasma gondii cysts
…automatic classification of mitochondrial morphologies using supervised machine learning to efficiently classify them from a large number of cells at a time. Fluorescence microscopy images of the chronic encysted form of parasite Toxoplasma gondii were used for this development. Manually…
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Machine learning approaches to predict age from accelerometer records of physical activity at biobank scale
…one causal factor for age-related diseases. In the following, we trained machine learning models to predict age from 115,456 one week-long wrist accelerometer recordings from participants of the UK Biobank. We then found genetic, environmental, and behavioral factors associated with accelerated…
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A performance comparison of machine learning models for stock market prediction with novel investment strategy
…impossible to predict the stock market with 100% accuracy. However, Machine Learning (ML) methods can improve stock market predictions to some extent. In this paper, a novel strategy is proposed to improve the prediction efficiency of ML models for financial markets. Nine ML models are used to…
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Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches
…testing. In this study, Utilizing several descriptors, we evaluated 27 machine learning classifiers. We also developed a neural network model that can correctly identify bioactive and inactive chemicals with 91% accuracy, on CheMBL data and 93% accuracy on combined data on both CheMBL and Pubchem…
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