Popular deep learning algorithms for disease prediction: a review

Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This pape...

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Vydáno v:Cluster computing Ročník 26; číslo 2; s. 1231 - 1251
Hlavní autoři: Yu, Zengchen, Wang, Ke, Wan, Zhibo, Xie, Shuxuan, Lv, Zhihan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2023
Springer Nature B.V
Témata:
Accuracy rate
Algorithms
Artificial intelligence
Artificial neural networks
Big Data
Cardiovascular disease
Computer Communication Networks
Computer Science
Convolution
Convolutional neural network
Convolutional neural networks
current
Data analysis
Data processing
Decomposition
Deep learning
Development history
Digital twins
Factorization machines
Feature learning
Forecasting
Learning abilities
Learning algorithms
Machine learning
Medical fields
Neural networks
Neurons
Operating Systems
Precision medicine
Processor Architectures
Recurrent neural networks
Support vector machines
Theory development
Trends
Artificial neural network
Factorization machine
Recurrent neural network
Convolutional neural network
ISSN:1386-7857, 1573-7543, 1573-7543
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Popis
Shrnutí:Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.
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ISSN:1386-7857
1573-7543
1573-7543
DOI:10.1007/s10586-022-03707-y