8.473 hasil untuk machine learning
Menampilkan 421–440 · 0,61 detik · RSS
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Immunophenotyping identifies key immune biomarkers for coronary artery disease through machine learning
…(KEGG) analyses were used to determine functional and pathways in CAD. Machine learning models were constructed for CAD prediction. Model validation was performed using GSE56885 and GSE71226 datasets. The expression and function of the identified genes were evaluated using immunohistochemistry…
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What factors enhance students’ achievement? A machine learning and interpretable methods approach
Prior research on student achievement has typically examined isolated factors or bivariate correlations, failing to capture the complex interplay between learning behaviors, pedagogical environments, and instructional design. This study addresses these limitations by employing an ensemble of five…
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Prediction of Poisson’s ratio for a petroleum engineering application: Machine learning methods
…model to predict the νs accurately using the nineteen most common learning methods. The proposed models were created based on a large data of 1691 datasets from different countries. The best-performing model of the nineteen models was selected and further enhanced using various approaches such as…
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Exploiting the similarity of dissimilarities for biomedical applications and enhanced machine learning
The “similarity of dissimilarities” is an emerging paradigm in biomedical science with significant implications for protein function prediction, machine learning (ML), and personalized medicine. In protein function prediction, recognizing dissimilarities alongside similarities provides a more…
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Development and validation of predictive models for diabetic retinopathy using machine learning
Objective: This study aimed to develop and compare machine learning models for predicting diabetic retinopathy (DR) using clinical and biochemical data, specifically logistic regression, random forest, XGBoost, and neural networks. Methods: A dataset of 3,000 diabetic patients, including 1,500 with…
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A recurrence model for non-puerperal mastitis patients based on machine learning
…aim of this research is to create and validate a recurrence model using machine learning for patients with non-puerperal mastitis. Methods: We retrospectively collected laboratory data from 120 NPM patients, dividing them into a non-recurrence group (n = 59) and a recurrence group (n = 61). Through…
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Predicting the diversity of photosynthetic light-harvesting using thermodynamics and machine learning
…We have developed a physical model of these antenna systems, along with a machine learning algorithm which is given a light input and evolves the antennae to better capture that light. We apply our algorithm to various kinds of light found in different places on Earth, finding that it predicts…
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Evaluating and implementing machine learning models for personalised mobile health app recommendations
…different health contexts such as ‘Weight loss’ and ‘Medical’. Various machine learning and deep learning models were evaluated, from the baseline Random Forest Classifier to the sophisticated BERT model. The results highlighted the efficiency of transfer learning, especially BERT, which achieved…
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Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach
…nutritional predictors implicated in the progression of LF using machine learning methods in the Ahanta West Municipality, Ghana. There were 109 participants with a mean age of 50.72±13.8, and three-quarters being females. Only 14 (12.8%) each had comorbidities or LF-related wounds. Nutrition risk…
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Automating the amino acid identification in elliptical dichroism spectrometer with Machine Learning
…complex and costly. Therefore, this study presents a pioneering Machine Learning (ML) approach for automatic amino acid identification by utilizing the unique absorption profiles from an Elliptical Dichroism (ED) spectrometer. Advanced data preprocessing techniques and ML algorithms to learn…
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Glaucoma detection and staging from visual field images using machine learning techniques
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern…
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Optimizing ensemble machine learning models for accurate liver disease prediction in healthcare
…to prevent severe liver damage. This study explores the application of machine learning (ML) techniques to predict liver disease, leveraging a dataset to compare the performance of several ensemble classifiers. The algorithms include the Random Forrest Classifier, Ada Boost Classifier, and Gradient…
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Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction
Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate noise in the training data, which may deteriorate machine learning model performance. Existing approaches addressing noisy training data typically rely…
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Correction: Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms
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Machine learning-based risk prediction model for cognitive dysfunction in elderly individuals
…to construct a risk prediction model for cognitive dysfunction based on machine learning (ML) algorithms, providing healthcare professionals and patients with a more accurate and effective tool for risk assessment. Methods: This study included 1,325 elderly participants who completed cognitive…
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Identifying daily-living features related to loneliness: A causal machine learning approach
…loneliness for the development of robust screening solutions using causal machine learning for health providers working with first-generation immigrants. Methods: We monitored 39 immigrants in Finland for 28 days using mobile devices and wearables under free-living conditions. Data included…
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Development of machine learning models for prediction of current and future dementia
…early diagnosis can delay or prevent disease progression. Previous machine learning-based prediction models have limitations (e.g., they are based on clinical parameters or are not generalizable). Thus, in this study, prediction models were developed for current and future dementia solely based on…
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Identifying influential determinants of women’s empowerment in Bangladesh using machine learning algorithms
…the influential determinants of women’s empowerment in Bangladesh using machine learning (ML) algorithms. Materials and methods: The data for this study were obtained from the Bangladesh Demographic and Health Survey (BDHS) 2022, which included a nationally representative sample of 18,600…
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M-polynomial driven machine learning models for predicting physicochemical properties of antibiotics
Accurate prediction of the physicochemical properties of drug compounds is critical for the development of effective and safe antibiotics. In this study, we employ advanced machine learning techniques to address this challenge, using input data that includes M-Polynomials and various…
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Single replica spin-glass phase detection using field variation and machine learning
…By analyzing the mean and variance of these local fields, we develop a machine learning algorithm to generate the phase diagram, which shows strong agreement with the theoretical solutions for the SK model. This algorithm offers a more computationally efficient approach for phase detection in…
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