1,721,049 research outputs found
MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING
No full text<p>MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING</p>
BLOCKCHAIN AND THE INTERNET OF THINGS (IOT): A CONVERGENCE OF TECHNOLOGIES
No full text<p>BLOCKCHAIN AND THE INTERNET OF THINGS (IOT): A CONVERGENCE OF TECHNOLOGIES</p>
MACHINE LEARNING EXPLAINED: A PRACTICAL GUIDE TO DATA-DRIVEN DECISION MAKING
No full text<p>MACHINE LEARNING EXPLAINED: A PRACTICAL GUIDE TO DATA-DRIVEN DECISION MAKING</p>
DEEP LEARNING ARCHITECTURES - NEURAL NETWORKS AND DEEP LEARNING MODELS
No full text<p>DEEP LEARNING ARCHITECTURES - NEURAL NETWORKS AND DEEP LEARNING MODELS</p>
REINFORCEMENT LEARNING FUNDAMENTALS - LEARNING THROUGH REWARDS AND PUNISHMENTS
No full text<p>REINFORCEMENT LEARNING FUNDAMENTALS - LEARNING THROUGH REWARDS AND PUNISHMENTS</p>
Predicting High-Risk Groups for COVID-19 Anxiety Using AdaBoost and Nomogram: Findings from Nationwide Survey in South Korea
No full textPeople living in local communities have become more worried about infection due to the extended pandemic situation and the global resurgence of COVID-19. In this study, the author (1) selected features to be included in the nomogram using AdaBoost, which had an advantage in increasing the classification accuracy of single learners and (2) developed a nomogram for predicting high-risk groups of coronavirus anxiety while considering both prediction performance and interpretability based on this. Among 210,606 adults (95,287 males and 115,319 females) in South Korea, 39,768 people (18.9%) experienced anxiety due to COVID-19. The AdaBoost model confirmed that education level, awareness of neighbors/colleagues’ COVID-19 response, age, gender, and subjective stress were five key variables with high weight in predicting anxiety induced by COVID-19 for adults living in South Korean communities. The developed logistic regression nomogram predicted that the risk of anxiety due to COVID-19 would be 63% for a female older adult who felt a lot of subjective stress, did not attend a middle school, was 70.6 years old, and thought that neighbors and colleagues responded to COVID-19 appropriately (classification accuracy = 0.812, precision = 0.761, recall = 0.812, AUC = 0.688, and F-1 score = 0.740). Prospective or retrospective cohort studies are required to causally identify the characteristics of anxiety disorders targeting high-risk COVID-19 anxiety groups identified in this study
Development of a Stacking-Based Ensemble Machine Learning for Detection of Depression in Parkinson’s Disease: Preliminary Research
No full textThis preliminary study used the stacking ensemble to explore the major elements (factors) which could predict depression in patients with Parkinson’s disease and presented baseline data for developing a nomogram prognostic index for predicting high-risk groups for depression among patients with Parkinson’s disease in the future. Depression, an outcome variable, was divided into “with depression” and “without depression” using the Geriatric Depression Scale-30 (GDS-30). This study developed nine machine learning models (ANN, random forest, naive bayes, CART, ANN+LR, random forest+LR, naive bayes+LR, CART+LR, and random forest+naive bayes+CART+ANN+LR). The predictive performance (e.g., REMS, IA, Ev) of each machine learning model was validated through 10-fold cross-validation. The analysis results showed that the random forest+LR had the best predictive performance: RMSE = 0.16, IA = 0.73, and Ev = 0.48. This study analyzed the normalized importance of the random forest+LR model’s variables (the final model) and confirmed that K-MMSE, K-MoCA, Global CDR, sum of boxes in CDR, total score of UPDRS, motor score of UPDRS, K-IADL, H and Y staging, Schwab and England ADL, and REM and RBD were ten major variables with high weight among predictors of Parkinson’s disease with depression in South Korea. It is necessary as well to develop interpretable machine learning to build a model for predicting depression in patients with Parkinson’s disease that can be used in the medical field
Entity embeddings, attention mechanisms and ordinal regression for predicting life satisfaction: a study of young south Korean male workers
Get PDFBackground: Young male workers in South Korea face unique challenges that can impact their subjective life satisfaction. This study aimed to develop and evaluate a novel machine learning model to predict their life satisfaction. Methods: We integrated Entity Embeddings with Attention mechanisms into an ordinal logistic regression framework. Entity Embeddings transformed categorical variables into dense vectors, while the Attention mechanism prioritized the most influential features. Model performance was compared against Random Forest, Gradient Boosting, and K-Nearest Neighbors (KNN) using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2). Results: The Entity Embeddings with Attention model demonstrated superior predictive accuracy compared to all baseline models across all evaluation metrics. Ordinal logistic regression facilitated model interpretability, revealing key predictors of life satisfaction, including monthly salary, job satisfaction, company size, weekly working hours, and educational background. Conclusions: This study provides valuable insights for policymakers and employers to enhance the well-being of young male workers in South Korea. The proposed model offers a robust and interpretable approach for predicting subjective life satisfaction, enabling targeted interventions to address the specific needs and challenges of this demographic
Exploring the Predictors of Rapid Eye Movement Sleep Behavior Disorder for Parkinson’s Disease Patients Using Classifier Ensemble
No full textThe rapid eye movement sleep behavior disorder (RBD) of Parkinson’s disease (PD) patients can be improved with medications such as donepezil as long as it is diagnosed with a thorough medical examination, since identifying a high-risk group of RBD is a critical issue to treat PD. This study develops a model for predicting the high-risk groups of RBD using random forest (RF) and provides baseline information for selecting subjects for polysomnography. Subjects consisted of 350 PD patients (Parkinson’s disease with normal cognition (PD-NC) = 48; Parkinson’s disease with mild cognitive impairment (PD-MCI) = 199; Parkinson’s disease dementia (PDD) = 103) aged 60 years and older. This study compares the prediction performance of RF, discriminant analysis, classification and regression tree (CART), radial basis function (RBF) neural network, and logistic regression model to select a final model with the best model performance and presents the variable importance of the final model’s variable. As a result of analysis, the sensitivity of RF (79%) was superior to other models (discriminant analysis = 14%, CART = 32%, RBF neural network = 25%, and logistic regression = 51%). It was confirmed that age, the motor score of Untitled Parkinson’s Disease Rating (UPDRS), the total score of UPDRS, the age when a subject was diagnosed with PD first time, the Korean Mini Mental State Examination, and Korean Instrumental Activities of Daily Living, were major variables with high weight for predicting RBD. Among them, age was the most important factor. The model for predicting Parkinson’s disease RBD developed in this study will contribute to the screening of patients who should receive a video-polysomnography
Relationship between Self-Perception of Weight and Depression Experience in Korean Adolescents
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