Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

Abstract

Many researchers are trying hard to minimize the incidence of cancers, especially GC. For GC, the five-year survival rate is generally 5–25%, but for EGC it can be reduced by up to 90%. Among the cancers, GC is more deadly. It is difficult for doctors to assess its threat to patients as it requires years of medical practice and rigorous testing. The health sector has benefitted from AI for the early diagnosis or classification of GC. However, the current AI-based techniques need to be further improved so they can be used in clinical testing. Heterogeneous GC characterization requires more optimized methods for early detection of GC because of its type and severity. Hence, it is important to further investigate this area and come up with more optimized approaches for early diagnosis. Early detection will increase the chances of successful treatments. In this study, we have conducted a literature survey detailing the role of AI in the healthcare sector for GC diagnosis. We discuss basic principles, advantages and disadvantages, training and testing of data, and integration of applications like DSS, CDSS, KDD, ML, DM, BD, and DL, and their relevance to the healthcare industry. The study focuses on the application of ML techniques used in the diagnosis of GC. This review paper also introduces DM techniques, how they are applied in the healthcare industry, their limitations, roles and, operational challenges. This will assist pathologists to help minimize their workload while increasing the diagnostic accuracy. These techniques will further assist medical practitioners with their decision-making process.

Topics & Concepts