Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients

Neto, Cristiana; Brito, Maria Ana de; Lopes, Vítor; Peixoto, Hugo; Abelha, António; Machado, José

doi:10.3390/e21121163

Cited by 33 publications

(15 citation statements)

References 37 publications

(36 reference statements)

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“…Machine learning and Data Mining (DM) are becoming increasingly important areas of engineering and computer science and have been successfully applied to a wide range of problems in science and engineering [34,35]. Machine Learning is the scientific field dealing with the construction of computer systems that have the ability to adapt, learn and improve their performance in a given domain through experience.…”

Section: Methodsmentioning

confidence: 99%

Recommendation System Using Autoencoders

et al. 2020

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The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

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Section: Methodsmentioning

confidence: 99%

Recommendation System Using Autoencoders

et al. 2020

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“…The useful trends will allow us to make nontrivial predictions about new data and are insightful in many significant ways about the already-observed data. However, to make matters worse, it is not always simple to identify these trends [26] [27].To succeed, we will have to use more sophisticated means of representing these systemic trends in results.…”

Section: Data Mining Implications In the Healthcare Sectormentioning

confidence: 99%

“…According to one study, 44,000 to 98,000 people die in the United States each year due to preventable medical errors (which account for 2-4% of all medical fatalities). Separate research found that up to 40% of patients were not receiving the appropriate treatment for chronic or acute illnesses [26].…”

Section: Limitations Of Data Mining In Healthcare Sectormentioning

confidence: 99%

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

Jamil

2022

IJCAI

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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.

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“…Data mining is a successful tool for extracting knowledge from large amounts of data. It is efficiently applied to many fields, such as weather forecasting [1], biomedical [2], medical diagnosis [3], marketing [4], security [5], and fraud detection [6]. On the other hand, sensitive data used in data mining applications or sensitive knowledge gained from these applications may cause privacy breaches directly or through linkable private data.…”

Section: Introductionmentioning

confidence: 99%

Hiding Sensitive Itemsets Using Sibling Itemset Constraints

2022

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Data collection and processing progress made data mining a popular tool among organizations in the last decades. Sharing information between companies could make this tool more beneficial for each party. However, there is a risk of sensitive knowledge disclosure. Shared data should be modified in such a way that sensitive relationships would be hidden. Since the discovery of frequent itemsets is one of the most effective data mining tools that firms use, privacy-preserving techniques are necessary for continuing frequent itemset mining. There are two types of approaches in the algorithmic landscape: heuristic and exact. This paper presents an exact itemset hiding approach, which uses constraints for a better solution in terms of side effects and minimum distortion on the database. This distortion creates an asymmetric relation between the original and the sanitized database. To lessen the side effects of itemset hiding, we introduced the sibling itemset concept that is used for generating constraints. Additionally, our approach does not require frequent itemset mining executed before the hiding process. This gives our approach an advantage in total running time. We give an evaluation of our algorithm on some benchmark datasets. Our results show the effectiveness of our hiding approach and elimination of prior mining of itemsets is time efficient.

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Application of Data Mining for the Prediction of Mortality and Occurrence of Complications for Gastric Cancer Patients

Cited by 33 publications

References 37 publications

Recommendation System Using Autoencoders

Recommendation System Using Autoencoders

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

Hiding Sensitive Itemsets Using Sibling Itemset Constraints

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