A survey on artificial intelligence techniques for chronic diseases: open issues and challenges

被引：0
作者：
Keyur Patel
Chinmay Mistry
Dev Mehta
Urvish Thakker
Sudeep Tanwar
Rajesh Gupta
Neeraj Kumar
机构：
[1] Nirma University,Department of Computer Science and Engineering
[2] Deemed to be University,Thapar Institute of Engineering and Technology
[3] Asia University,Department of Computer Science and Information Engineering
[4] School of Computer Science,undefined
[5] University of Petroleum and Energy Studies,undefined
来源：
Artificial Intelligence Review | 2022年 / 55卷
关键词：
Artificial intelligence; Deep learning; Support vector machine; Convolutional neural network; Artificial neural network; Deep belief network; Chronic diseases; Cancer; Brian-related diseases; Heart-related diseases;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Artificial Intelligence (AI) has given significant solutions to the healthcare domain for analyzing various chronic diseases. With the advent of high-end systems, i.e., Graphics Processing Units, AI widespread the healthcare domain, where human experts dominate. AI techniques make the early identification and diagnosis of diseases, which aid the clinicians in mitigating the associated risk. This survey comprehensively reviews the existing literature on AI-assisted chronic disease prediction by considering cancer, heart, and brain-related diseases. However, research is underway to design and develop efficient AI techniques to aid the early prediction of diseases and render valuable insights into the patient’s profile. We conclude with the open issues and challenges faced by the current AI techniques for the prediction and early detection of chronic diseases and discuss future work in the diagnosis of these diseases.
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页码：3747 / 3800
页数：53
相关论文
共 520 条
[61]  
Genc A(2010)Comparison of adaboost and support vector machines for detecting alzheimer’s disease through automated hippocampal segmentation IEEE Trans Med Imag 29 30-393
[62]  
Xu D(1997)Mortality by cause for eight regions of the world: Global burden of disease study Lancet 349 1269-90519
[63]  
Resnick SM(2019)Detection of skin cancer using svm, random forest and knn classifiers J Med Syst 43 269-63
[64]  
Dramiński M(1994)The abcd rule of dermatoscopy: high prospective value in the diagnosis of doubtful melanocytic skin lesions J Am Acad Dermatol 30 551-25
[65]  
Rada-Iglesias A(2016)Breast cancer prediction using data mining techniques Int J Recent Innov Trends Comput Commun 4 55-654
[66]  
Enroth S(2014)Investigating the performance improvement of hrv indices in chf using feature selection methods based on backward elimination and statistical significance Comput Biol Med 45 72-775
[67]  
Wadelius C(2020)The impact of patient clinical information on automated skin cancer detection Comput Biol Med 116 103545-215
[68]  
Koronacki J(2008)Brain tumor characterization using the soft computing technique of fuzzy cognitive maps Appl Soft Comput 8 820-4072
[69]  
Komorowski J(2015)Automated leukaemia detection using microscopic images Proc Comput Sci 58 635-167
[70]  
El-Regaily SA(2019)Global, regional, and national burden of brain and other cns cancer, 1990–2016: a systematic analysis for the global burden of disease study 2016 Lancet Neurol 18 376-182
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