Machine Learning in Detection of Disease: Solutions and Open Challenges

- Authors: Tayyab Rehman1, Noshina Tariq2, Ahthasham Sajid3, Muhammad Hamza Akhlaq4
-
View Affiliations Hide Affiliations1 Faculty of Computing, SZABIST, Islamabad, Pakistan 2 Faculty of Computing, SZABIST, Islamabad, Pakistan 3 Department of Computer Science, Faculty of ICT, BUITEMS, Quetta, Baluchistan, Pakistan 4 Department of Computer Science, Allama Iqbal Open University, Islamabad, Pakistan
- Source: Machine Intelligence for Internet of Medical Things: Applications and Future Trends , pp 149-176
- Publication Date: May 2023
- Language: English
Disease diagnosis is the most important concern in the healthcare field. Machine Learning (ML) classification approaches can greatly improve the medical industry by allowing more accurate and timely disease diagnoses. Recognition and machine learning promise to enhance the precision of diseases assessment and treatment in biomedicine. They also help make sure that the decision-making process is impartial. This paper looks at some machine learning classification methods that have remained proposed to improve healthcare professionals in disease diagnosis. It overviews machine learning and briefly defines the most used disease classification techniques. This survey paper evaluates numerous machine learning algorithms used to detect various diseases such as major, seasonal, and chronic diseases. In addition, it studies state-of-the-art on employing machine learning classification techniques. The primary goal is to examine various machine-learning processes implemented around the development of disease diagnosis and predictions.
-
From This Site
/content/books/9789815080445.chap10dcterms_subject,pub_keyword-contentType:Journal -contentType:Figure -contentType:Table -contentType:SupplementaryData105
