Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Most biomedical datasets, including those of ‘omics, population studies, and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms....

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Bibliographic Details
Published in:	Laboratory investigation Vol. 101; no. 4; pp. 430 - 441
Main Authors:	Deng, Fei, Huang, Jibing, Yuan, Xiaoling, Cheng, Chao, Zhang, Lanjing
Format:	Journal Article
Language:	English
Published:	New York Elsevier Inc 01.04.2021 Nature Publishing Group US Nature Publishing Group
Subjects:	14/105 631/1647/48 692/308/2056 Accuracy Aged Algorithms Artificial neural networks Biomedical data Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - epidemiology Cardiovascular diseases Classification Databases, Factual Datasets Decision trees Diagnosis, Computer-Assisted - methods Efficiency Entropy (Information theory) Epidemiology Female Humans Laboratory Medicine Learning algorithms Learning theory Machine Learning Medicine Medicine & Public Health Middle Aged Missing data Model accuracy Neural networks Pathology Population studies Reduction Regression analysis Reproducibility of Results Support Vector Machine Support vector machines
ISSN:	0023-6837, 1530-0307, 1530-0307
Online Access:	Get full text
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