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, 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.

Reducing the Risk of Gastric Cancer Through Proper Nutrition – A Meta-Analysis

“…A low nutritional status influences treatment, method compliance, and drug response. The author [147], found a relationship between patient quality of life and dietetic education. Researchers found that patients who received information and nutritional counselling throughout chemotherapy were less likely to lose significant weight and had a higher overall quality of life [16] [147].…”

“…In the development of GC, there are substantial research gaps in areas such as nutrients, as shown in articles included in this collection of this research. According to the finding of author [147],the systematic reviews, the diversity across research made it difficult to reach meaningful conclusions. By using data mining algorithms, physicians and patients can monitor the course of GC and take appropriate treatments to improve the patients' chances of survival and well-being, particularly if they are anticipated to be at high risk of developing EGC [147].…”

“…According to the finding of author [147],the systematic reviews, the diversity across research made it difficult to reach meaningful conclusions. By using data mining algorithms, physicians and patients can monitor the course of GC and take appropriate treatments to improve the patients' chances of survival and well-being, particularly if they are anticipated to be at high risk of developing EGC [147]. Next point is to search whether dietary interventions might improve cancer patients' quality of life (QoL) by conducting a comprehensive review of the existing research.…”

Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

“…The presented techniques aim to identify stomach diseases in less computation time with higher accuracy. Different deep-learning techniques [ 18 ] equipped with feature optimization approaches for robust disease recognition have been utilized by the researcher [ 19 ]. In [ 20 ], the authors introduced a method for the recognition of bleeding and ulcers in WCE images.…”

IoT-Enabled Remote Monitoring Techniques for Healthcare Applications -- An Overview

“…It could be noticed that to improve the prediction accuracy in remote health monitoring applications cloud services requires long-term analytics obtained from a larger dataset. For instance, bots increase accuracy by improving text analysis strategies; analytics of images is required for classifying, learning, or predicting health-related symptoms such as tumor analysis, cancer analysis [50,21], eyeretinal failure detections, and so forth; analytics of videos is required for learning the emotions of patients, neurological disorders, and sleep disorders.…”