Vol.128, No.2, 2021, pp.489-522, doi:10.32604/cmes.2021.016728
OPEN ACCESS
REVIEW
Multi-Disease Prediction Based on Deep Learning: A Survey
  • Shuxuan Xie, Zengchen Yu, Zhihan Lv*
College of Data Science Software Engineering, Qingdao University, Qingdao, 2660712, China
* Corresponding Author: Zhihan Lv. Email:
Received 20 March 2021; Accepted 06 May 2021; Issue published 22 July 2021
Abstract
In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI’s research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research.
Keywords
Deep learning; disease prediction; Internet of Things; COVID-19; precision medicine
Cite This Article
Xie, S., Yu, Z., Lv, Z. (2021). Multi-Disease Prediction Based on Deep Learning: A Survey. CMES-Computer Modeling in Engineering & Sciences, 128(2), 489–522.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.