TY - JOUR
T1 - Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms
T2 - Principles and Perspectives
AU - Kaur, Simarjeet
AU - Singla, Jimmy
AU - Nkenyereye, Lewis
AU - Jha, Sudan
AU - Prashar, Deepak
AU - Joshi, Gyanendra Prasad
AU - El-Sappagh, Shaker
AU - Islam, Md Saiful
AU - Riazul Islam, S. M.
N1 - Funding Information:
This work was supported in part by the National Research Foundation of Korea grant funded by the Korean Government, Ministry of Science and ICT, under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program.
Publisher Copyright:
© 2013 IEEE.
PY - 2020/12/31
Y1 - 2020/12/31
N2 - Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today's medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.
AB - Disease diagnosis is the identification of an health issue, disease, disorder, or other condition that a person may have. Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. There are large data sets available; however, there is a limitation of tools that can accurately determine the patterns and make predictions. The traditional methods which are used to diagnose a disease are manual and error-prone. Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. In this paper, we have reviewed the current literature for the last 10 years, from January 2009 to December 2019. The study considered eight most frequently used databases, in which a total of 105 articles were found. A detailed analysis of those articles was conducted in order to classify most used AI techniques for medical diagnostic systems. We further discuss various diseases along with corresponding techniques of AI, including Fuzzy Logic, Machine Learning, and Deep Learning. This research paper aims to reveal some important insights into current and previous different AI techniques in the medical field used in today's medical research, particularly in heart disease prediction, brain disease, prostate, liver disease, and kidney disease. Finally, the paper also provides some avenues for future research on AI-based diagnostics systems based on a set of open problems and challenges.
KW - artificial intelligence
KW - Big data analytics
KW - chronic disease
KW - deep learning
KW - diagnosis
KW - health care prediction
KW - machine learning
KW - soft computing
UR - http://www.scopus.com/inward/record.url?scp=85097951867&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3042273
DO - 10.1109/ACCESS.2020.3042273
M3 - Article
AN - SCOPUS:85097951867
VL - 8
SP - 228049
EP - 228069
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9279211
ER -
