Machine learning in expert systems for disease diagnostics in human healthcare

Abstract Expert systems are a rapidly emerging technology in the field of artificial intelligence (AI), having an immense impact on human healthcare. The main objective of a medical expert system is to help medical professionals in arriving at correct diagnostics. Information gathering is an important part of disease diagnosis, and traditional methods of diagnosis are very time-consuming and require a high level of expertise. Expert systems are computer systems that attempt to imitate the ability of human diagnostic decision-making. They employ knowledge about diseases and facts about patients as data and suggest diagnoses by using machine-learning methods. Expert systems offer suggestions to physicians/area experts in order to improve the expert’s ability and increase the consistency and quality of diagnostics. This type of system is also very helpful for patients who are unable to reach a doctor due to cost, being in a remote area, or feeling too ashamed to discuss their circumstances with a doctor. The expert system also helps to improve decision quality, reduce cost, and maintain consistency, reliability, and speed of diagnosis. There are various learning-based expert systems for different diseases, developed to help doctors and to serve in disease diagnosis. This chapter discusses important existing expert systems for human disease diagnosis in detail. It also provides a brief evaluation of various techniques used in the development of expert systems.

• Publication year

  2021

• Venue

  Machine Learning, Big Data, and IoT for Medical Informatics

• Publication date

  Unknown publication date

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1016/b978-0-12-821777-1.00022-7
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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