Cascade-Forward Algorithm to Extract Hidden Rules of Gastric Cancer Information 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. Although the use of traditional data mining techniques such as association rules helps to extract knowledge from large data sets, sometimes the results obtained from a data set are so large that it is a major problem. In fact, one of the disadvantages of this technique is a lot of nonsense and redundant rules due to the lack of attention to the concept and meaning of items or the samples. This paper presents a new method to discover association rules using ontology to solve the expressed problems. This paper reports a data mining based on ontology on a medical database containing clinical data on patients referring to the Imam Reza Hospital at Tabriz. The data set used in this paper is gathered from 490 random visitors to the Imam Reza Hospital at Tabriz, who had been suspicions of having gastric cancer. The proposed data mining algorithm based on ontology makes rules more intuitive, appealing and understandable, eliminates waste and useless rules, and as a minor result, significantly reduces Apriori algorithm running time. The experimental results confirm the efficiency and advantages of this algorithm.