Corrigendum to "A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm".

[This corrects the article DOI: 10.1155/2021/1203726.].

A Dynamic Model for Imputing Missing Medical Data: A Multiobjective Particle Swarm Optimization Algorithm.

Missing data occurs in all research, especially in medical studies. Missing data is the situation in which a part of research data has not been reported. This will result in the incompatibility of the sample and the population and misguided conclusions.

A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map.

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC.

A new algorithm to extract hidden rules of gastric cancer data based on ontology.

Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Gastric cancers are among the most devastating and incurable forms of cancer and their treatment may be excessively complex and costly. Data mining, a technology that is used to produce analytically useful information, has been employed successfully with medical data.