Role of AI techniques and deep learning in analyzing the critical health conditions
作者： Shilpa Srivastava , Millie Pant , Ritu Agarwal
DOI: 10.1007/S13198-019-00863-0
关键词:
摘要: The role of a healthcare practitioner is to diagnose disease and find an optimum means for suitable treatment. This has been the most challenging task over years. researchers have developing intelligent tools providing support in taking medical decision. application AI different health scenario strengthen mechanism finding better treatment plan. authors share some recent advancements this domain. artificial intelligence Indian system also discussed. paper analyzes use techniques like fuzzy logic, Artificial Neural Networks, Particle Swarm Optimization Fuzzy critical scenario. A detail literature review provided context. taken into consideration are cancer, TB, diabetes, malaria, orthopedics, obesity specific elderly people. purpose article relevance various scenarios. comparative analysis done based on publications since 1995. challenges risks associated with usage analysed suggestions made making domain more accurate effective. Further concept deep learning explained its inculcation
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sci-hub.se 参考文章(98)
Mohamed Roushdy, Mohamed A. Madkour, Methodology for Medical Diagnosis based on Fuzzy Logic. Egyptian Computer Science Journal. ,vol. 26, ,(2004)
Chang-Yu Wang, Jinn-Tsong Tsai, Chun-Hsiung Fang, Tsair-Fwu Lee, Jyh-Horng Chou, Predicting survival of individual patients with esophageal cancer by adaptive neuro-fuzzy inference system approach soft computing. ,vol. 35, pp. 583- 590 ,(2015) , 10.1016/J.ASOC.2015.05.045
Mumini Olatunji Omisore, Oluwarotimi Williams Samuel, Edafe John Atajeromavwo, A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis Applied Computing and Informatics. ,vol. 13, pp. 27- 37 ,(2017) , 10.1016/J.ACI.2015.06.001
H. Lookman Sithic, R. UmaRani, Fuzzy Matrix Theory as a Knowledge Discovery in Health Care Domain Procedia Computer Science. ,vol. 47, pp. 282- 291 ,(2015) , 10.1016/J.PROCS.2015.03.208
P. Bhuvaneswari, A. Brintha Therese, Detection of Cancer in Lung with K-NN Classification Using Genetic Algorithm Procedia Materials Science. ,vol. 10, pp. 433- 440 ,(2015) , 10.1016/J.MSPRO.2015.06.077
Shaker El-Sappagh, Mohammed Elmogy, A.M. Riad, A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis Artificial Intelligence in Medicine. ,vol. 65, pp. 179- 208 ,(2015) , 10.1016/J.ARTMED.2015.08.003
Rian Budi Lukmanto, E. Irwansyah, The Early Detection of Diabetes Mellitus (DM) Using Fuzzy Hierarchical Model Procedia Computer Science. ,vol. 59, pp. 312- 319 ,(2015) , 10.1016/J.PROCS.2015.07.571
Lidia Ostrowska-Nawarycz, Wojciech Drygas, Krzysztof Pytel, Tadeusz Nawarycz, Maciej Gazicki-Lipman, A fuzzy logic approach to the evaluation of health risks associated with obesity federated conference on computer science and information systems. pp. 231- 234 ,(2013)
Zeljko Knok, Zikrija Avdagic, Samir Omanovic, Hybride neuro-fuzzy expert system for assessing diabetes risk international convention on information and communication technology electronics and microelectronics. pp. 1179- 1182 ,(2015) , 10.1109/MIPRO.2015.7160454
Hazlina Hamdan, Jonathan M Garibaldi, None, Automatic Generation of ANFIS Rules in Modelling Breast Cancer Survival 2014 International Conference on Computer Assisted System in Health. pp. 12- 17 ,(2014) , 10.1109/CASH.2014.16