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Showing 1 to 11 of 11 for “"Brier score"”.
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Effects of Focal on Driver Calibration of Attention Maintenance Performance Using Normalized Difference and Brier Scores
… two different measures, normalized difference scores and the Brier score. Thirty-six participants received either FOCAL or Placebo training program, immediately followed by driving simulator evaluation of their attention maintenance performance. In the evaluation drive, participants had driven …
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Nonparametric efficient estimation of prediction error for incomplete data models
… identifiable with censored <br>observations. The Brier score is a loss function which is suitable for <br>the assessment of predictions made in terms of predicted probabilities <br>that are coming from a regression model or other sources. <br> <br>This thesis is on defining and estimating measures …
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Measures of discrimination, reclassification, and calibration for risk prediction models: an exploration in their interrelationships and practical utility and improvement in their estimation
… these qualities include (but are not limited to) Brier score, calibration-in-the-large, proportion of variation (R2), sensitivity and specificity, area under the receiver operating characteristic curve (AUC), discrimination slope, net reclassification index (NRI), integrated discrimination …
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The Effect of Hypertension on Periapical Disease: a Retrospective Study Utilizing Machine Learning to Assess a Dental School Population
… machine learning algorithms, XGB had the lowest brier score, highest accuracy and highest AUC ROC values. The mean brier score, accuracy, and AUC ROC was 0.137, 83.73%, and 88.59% respectively. XGB found “age”, “sex”, “high blood pressure”, and “Insurance – Medicaid” to be the most significant …
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Comparison of Some Statistical and Machine Learning Models for Continuous Survival Analysis
… Model performance was evaluated using Integrated Brier score (IBS), Area Under the Curve and Concordance index. Our findings shows consistent dominance of Neural Network (NN) and Random Survival Forest (RSF) models across multiple metrics for both datasets. Specifically, Neural Network …
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Modelling first innings totals in T20 cricket: applications in the Indian Premier League
… a model that will allow teams to determine what scores they would need to score for any given confidence of avoiding defeat in regular time, viz. before any Super Overs. The following machine learning methods are considered for this purpose: logistic regression, classification trees, bagging, …
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NBA Machine Learning for Game Outcome Prediction
… is built from publicly available NBA box score data and engineered to avoid look-ahead bias. Several feature sets are compared, including a baseline team Elo logistic regression, an expanded model incorporating player-level Elo, and a final specification that adds rolling box score …
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Can we predict when to start renal replacement therapy in chronic kidney disease patient by using 6 months clinical data?
… by using integral means. From the result of brier score (BS) and area under the curve (AUC), we consider that our model have significant explanatory power to predict the probability and interval time to start RRT.
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Identification and Characterization of De Novo Germline Tp53 Mutation Carriers In Families With Li-Fraumeni Syndrome
… index (AUC), observed:expected ratios (OE) and Brier score (BS) to measure our model’s discrimination, calibration and accuracy, respectively. We use our model to analyze 101 families recently collected from the Clinical Cancer Genetic program at MD Anderson Cancer Center (CCG-MDA). We vii …
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A Comparative Analysis of Machine Learning Models and Traditional Statistical Models for Continuous-Time Survival Analysis
… the concordance index (C-index), integrated Brier score (IBS), and Time-dependent Area Under the Curve (AUC) across three secondary datasets with different characteristics: a breast cancer dataset obtained from the SEER Program of the National Cancer Institute (2017 November update), the …
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Machine learning models for functional impairment risk prediction in ischemic stroke patients
… Stroke-related functional impairment risk scores are commonly used to estimate the patient-specific risk of functional impairment in acute care settings. However, these models have been primarily developed based on regression models, which might not provide optimal predictive accuracy, …