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.
Highlights
According to the US Cancer Society, approximately 28,000 people lived with cancer in 2019, accounting for 1.7% of all cancer cases, while 10,960 people died from GC[1].In most parts of the African regions, there was a low risk of GC[2], though the rate of GC has fallen in recent decades, and it is the world's third leading cause of cancer deaths, after lung cancer and colorectal cancer
The findings showed that 95% of the improved performance in the data set through an algorithm
The primary challenge with BD in healthcare is making the data simple to interpret, which is beneficial for medical practitioners since it is a tool for detecting significant patterns in complex data
Summary
According to the US Cancer Society, approximately 28,000 people lived with cancer in 2019, accounting for 1.7% of all cancer cases, while 10,960 people died from GC[1].In most parts of the African regions, there was a low risk of GC[2], though the rate of GC has fallen in recent decades, and it is the world's third leading cause of cancer deaths, after lung cancer and colorectal cancer. The risk factors associated with cancer will increase the chances of a patient getting GC. Many hospitals produce a large amount of redundant data; most of the data is ambiguous and low quality due to its missing values. This heterogeneity of data contributes to the need for a comprehensive review of data to determine its output and recognize its potential issues. Deep learning (DL); its purpose is to identify patterns and get data from prior occurrences.ML benefits people in various ways, including identifying cancerous cells, recognizing hacker or lawbreaker patterns in massive amounts of monetary transactions, performing speech and video recognition, and developing chatbots that speak and understand human speech to communicate better. Their purpose is to utilize these machines for data gathering and data
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