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Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

Benjamin A. Goldstein, Ann Marie Navar, Rickey E. Carter
2016 European Heart Journal  
Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. ---  ...  The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models.  ...  We consider machine-learning algorithms to be any approach that performs an automated search, either stochastic or deterministic, for the optimal model.  ... 
doi:10.1093/eurheartj/ehw302 pmid:27436868 pmcid:PMC5837244 fatcat:zwg657c35rgszfohcrupmxvta4

Don't dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning

Joshua J. Levy, A. James O'Malley
2020 BMC Medical Research Methodology  
from a random forest model for inclusion in the model.  ...  When a random forest model is closer to the true model, hybrid statistical-machine learning procedures can substantially enhance the performance of statistical procedures in an automated manner while preserving  ...  The data used for evaluating the machine learning benchmark datasets are openly available for download at https://www.openml.org/ .  ... 
doi:10.1186/s12874-020-01046-3 pmid:32600277 fatcat:xvvsfumrerdt7j7z43i5q5rjty

Don't Dismiss Logistic Regression: The Case for Sensible Extraction of Interactions in the Era of Machine Learning [article]

Joshua J Levy, James O'Malley
2019 bioRxiv   pre-print
features from a random forest model for inclusion in the model.  ...  Conclusions: When a random forest model is closer to the true model, hybrid statistical - machine learning procedures can substantially enhance the performance of statistical procedures in an automated  ...  Acknowledgements The data used for evaluating the machine learning benchmark datasets are openly available for download at https://www.openml.org/ .  ... 
doi:10.1101/2019.12.15.877134 fatcat:hhg73c3esrfkdcvekgakuw3gzq

Predictors of tooth loss: A machine learning approach

Hawazin W. Elani, André F. M. Batista, W. Murray Thomson, Ichiro Kawachi, Alexandre D. P. Chiavegatto Filho, Khanh N.Q. Le
2021 PLoS ONE  
The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models.  ...  Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions.  ...  PLOS ONE Predictors of tooth loss: A machine learning approach Discussion To the best of our knowledge, this is the first use of machine-learning algorithms to predict complete and incremental tooth  ... 
doi:10.1371/journal.pone.0252873 pmid:34143814 pmcid:PMC8213149 fatcat:2ap2ifcj55gobclijol6gl2ndq

BRAINSTORMING: Consensus Learning in Practice [article]

Dariusz Plewczynski (ICM, Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Pawinskiego 5a Street, 02-106 Warsaw, Poland)
2009 arXiv   pre-print
Therefore no early solution, given even by a generally low performing algorithm, is not discarder until the late phase of prediction, when the final conclusion is drawn by comparing different machine learning  ...  This final phase, i.e. consensus learning, is trying to balance the generality of solution and the overall performance of trained model.  ...  Our experience clearly supports the idea that each machine learning algorithm is performing better for selected types of training data [20, 40] .  ... 
arXiv:0910.0949v1 fatcat:rdxvasolgrhpbatkhhuxpdx6ym

Learning Predictors from Multidimensional Data with Tensor Factorizations

Soo Min Kwon, Anand D. Sarwate
2021 Aresty Rutgers Undergraduate Research Journal  
Statistical machine learning algorithms often involve learning a linear relationship between dependent and independent variables.  ...  This relationship is modeled as a vector of numerical values, commonly referred to as weights or predictors.  ...  There are many different machine learning classification algorithms, and each algorithm has a different loss function.  ... 
doi:10.14713/arestyrurj.v1i3.165 fatcat:ycviyileazavthlnyyg3flebea

Super learning for daily streamflow forecasting: Large-scale demonstration and comparison with multiple machine learning algorithms [article]

Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis
2019 arXiv   pre-print
Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm.  ...  Here we propose super learning (a type of ensemble learning) by combining 10 machine learning algorithms. We apply the proposed algorithm in one-step ahead forecasting mode.  ...  Note, however, that existing approaches to daily streamflow forecasting are mostly based on the implementation of a single machine learning algorithm.  ... 
arXiv:1909.04131v1 fatcat:r337zbiv6jeldarewdabynev7u

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

Anita L. Lynam, John M. Dennis, Katharine R. Owen, Richard A. Oram, Angus G. Jones, Beverley M. Shields, Lauric A. Ferrat
2020 Diagnostic and Prognostic Research  
We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors  ...  We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes).  ...  We thank Catherine Angwin of the NIHR Exeter Clinical Research Facility for the assistance with data preparation, and Rachel Nice of the Blood Sciences Department, Royal Devon and Exeter Hospital for the  ... 
doi:10.1186/s41512-020-00075-2 pmid:32607451 pmcid:PMC7318367 fatcat:jg4whrmkzvfgfejuqhqu3kbxwy

Statistically reinforced machine learning for nonlinear patterns and variable interactions

Masahiro Ryo, Matthias C. Rillig
2017 Ecosphere  
The applications of statistically reinforced machine learning approaches would be particularly beneficial for investigating (1) novel patterns for which shapes cannot be assumed a priori, (2) higher-order  ...  We here introduce a set of novel empirical modeling techniques which can address this mismatch: statistically reinforced machine learning.  ...  A new movement that develops algorithms merging the two relevant approaches-statistical modeling and machine learning-has been occurring since the mid-2000s.  ... 
doi:10.1002/ecs2.1976 fatcat:w77ryefvpfepjjm7tm42n6dotu

Artificial or intelligent? Machine learning and medical selection: possibilities and risks

Paul Tiffin, Lewis Paton
2018 MedEdPublish  
To date, relatively little research has been published on the potential for machine learning to support personnel selection.  ...  Nevertheless, there are both general limitations to developing and implementing machine learning approaches that must be borne in mind.  ...  LP is partly funded via a research grant from the UKCAT Board. AMEE MedEdPublish: rapid, post-publication, peer-reviewed papers on healthcare professions' education.  ... 
doi:10.15694/mep.2018.0000256.1 fatcat:zdsukq37mna4rcgwoed4g52i3m

Automated interpretable computational biology in the clinic: a framework to predict disease severity and stratify patients from clinical data

Soumya Banerjee
2017 Interdisciplinary Description of Complex Systems  
Our models are also interpretable, allowing clinicians with minimal machine learning experience to engage in model building. This work is a step towards automated machine learning in the clinic.  ...  Insights from machine learning algorithms coupled with clinical data may help guide therapy, personalize treatment and help clinicians understand the change in disease over time.  ...  Stephen Haben for fruitful discussions.  ... 
doi:10.7906/indecs.15.3.4 fatcat:rejomqxspbcbbhuohxfavhohky

How Long Will My Mouse Live? Machine Learning Approaches for Prediction of Mouse Life Span

W. R. Swindell, J. M. Harper, R. A. Miller
2008 The journals of gerontology. Series A, Biological sciences and medical sciences  
In this study, we used machine learning algorithms to construct models that predict lifespan in a stock of genetically heterogeneous mice.  ...  Lifespanprediction accuracy of 22 algorithms was evaluated using a cross-validation approach, in which models were trained and tested with distinct subsets of data.  ...  Acknowledgements We thank Maggie Lauderdale and Jessica Sewald for technical and husbandry assistance. We also thank two anonymous reviewers for helpful comments on this manuscript.  ... 
doi:10.1093/gerona/63.9.895 pmid:18840793 pmcid:PMC2693389 fatcat:zpcylalfqze3lmi2f5gp4zhfsy

Mapping parallelism to multi-cores

Zheng Wang, Micheal F.P. O'Boyle
2008 Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming - PPoPP '09  
to other techniques  Let an off-line machine learning model build heuristics for us.  ...  overhead • Reduce the profiling cost for a new program by a factor between 4x and 512x  On average, the data insensitive (DI) predictor performs as well as the data sensitive (DS) predictor  ... 
doi:10.1145/1504176.1504189 dblp:conf/ppopp/WangO09 fatcat:mu334bulfbhpnm4gthbpdb5b7u

A systematic review on machine learning in sellar region diseases: quality and reporting items

Nidan Qiao
2019 Endocrine Connections  
Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility.  ...  learning models.  ...  Acknowledgement Research involving human participants and/or animals: this article does not contain any studies with human participants performed by any of the authors.  ... 
doi:10.1530/ec-19-0156 pmid:31234143 pmcid:PMC6612064 fatcat:ocqonmmwfnba3de2j4axq7qq3u

Predicting Global Irradiance Combining Forecasting Models Through Machine Learning [chapter]

J. Huertas-Tato, R. Aler, F. J. Rodríguez-Benítez, C. Arbizu-Barrena, D. Pozo-Vázquez, I. M. Galván
2018 Lecture Notes in Computer Science  
Three approaches are studied: a general model, a model for each horizon, and models for groups of horizons.  ...  Experimental results show that the machine learning combination of predictors is, on average, more accurate than the predictors themselves.  ...  which are used as inputs for the machine learning algorithm.  ... 
doi:10.1007/978-3-319-92639-1_52 fatcat:yheejbynorb75ahw3pe6ydoywi
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