Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Mar 10:5:312.
doi: 10.1186/s40064-016-1943-9. eCollection 2016.

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

Seyed Abbas Mahmoodi  1 , Kamal Mirzaie  2 , Seyed Mostafa Mahmoudi  3
Affiliations

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

Seyed Abbas Mahmoodi et al. Springerplus. .

Abstract

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.

Keywords: Apriori; Data mining; Gastric cancer; Ontology.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
Ontology design process
Fig. 2
Fig. 2
Disease ontology
Fig. 3
Fig. 3
Cancer ontology
Fig. 4
Fig. 4
Gastric cancer ontology
Fig. 5
Fig. 5
Ontology of the risk factors of gastric cancer
Fig. 6
Fig. 6
Concepts and relationships of the ontologies
Fig. 7
Fig. 7
Workflow of the proposed algorithm
Fig. 8
Fig. 8
Algorithm process
Fig. 9
Fig. 9
Itemset, item structure
Fig. 10
Fig. 10
Run time
See this image and copyright information in PMC

References

    1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases. VLDB, Santiago, Chile
    1. Alizadehsani R, Habibi J, Hosseini MJ. A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed. 2013;111(1):269–282. doi: 10.1016/j.cmpb.2013.03.004. - DOI - PubMed
    1. Bathaie SZ, Mohagheghi MA. Saffron aqueous extract inhibits the chemically-induced gastric cancer progression in the Wistar albino rat. Iran J Basic Med Sci. 2012;16(1):27–38. - PMC - PubMed
    1. Buitelar P, Cimiano P. Ontology learning and population: bridging the gap between text and knowledge. Amsterdam: IOS Press; 2008. p. 167.
    1. Carr JS, Zafar SF, Saba N, Khuri FR. risk factors for rising incidence of esophageal and gastric cardia adenocarcinoma. J Gastrointest Cancer. 2013;44:143–151. doi: 10.1007/s12029-013-9480-z. - DOI - PubMed