Many researchers are trying hard to minimize the incidence of cancers, mainly GC. For GC, the five-year survival rate is generally 5-25%, but for EGC, it is almost up to 90%. Among the cancers, GC is very deadly. It is difficult for doctors to assess its threat to patients as it requires years of medical practice and rigorous testing. The healthcare sector has benefitted from AI for the early diagnosis or classification of GC. However, the current AI-based techniques need further improvement in clinical testing. Heterogeneous GC characterization requires more optimized methods for early detection of GC because of its type and severity. Hence, it is essential to investigate this area further and develop 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, their application in the healthcare industry, limitations, roles, and operational challenges. These assist pathologists in helping minimize their workload while increasing diagnostic accuracy. These techniques will further assist medical practitioners with their decision-making process. Povzetek: Raziskava o uporabi tehnik strojnega učenja pri diagnostiki raka želodca v zdravstvenem sektorju.
Abstract— Data Sources: Articles for this paper were sourced from PubMed and Google Scholar databases. Data Extraction: Human studies published in English from 2015 to 2021 were included. Two reviewers initially assessed abstracts of 958 papers and 192 papers were selected for future assessments. After a full review, 38 articles were selected. Results: Dietary salt is a high-risk factor for Gastric Cancer (GC) while red meat and a high-fat diet increase the risk. Alcohol intake is only a risk if consumed heavily.
Conclusion: Dairy foods, vitamin C, smoked food, rich food, and Helicobacter pylori are included in the etiology of GC. So are fresh fruits, vegetables, and specific refined grains. Salt, fats, spicy foods, alcohol, red meat, and pepper increase the risk. Knowing cancer patients' nutrition problems would help intervention measures be taken to remedy the situation.
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