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. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

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. 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.

Prevalence of Head and Neck Tumors in Children under 12 Years of Age Referred to the Pathology Department of Children's Hospital in Tabriz during a 10-year Period.

Background and aims. Head and neck tumors are the most common complaints of people referring to different medical sections, especially in children. The aim of this study was to evaluate the prevalence of these tumors in children less than 12 years of age to provide a better perspective for future studies. Materials and methods. All the files in Department of Pathology at Tabriz Pediatric Hospital from 2001 to 2011 were screened for head and neck tumors in children under 12 years of age. Data including age and gender as well as the type, the location, and benign/malignant characteristic of the tumor were recorded. Data were analyzed by SPSS 15 statistical software, using descriptive statistics and chi-square test. Results. A total of 160 cases were identified. Most of the tumors were benign (68%) and most of the tumors occurred in the neck region (41%). The most frequent benign and malignant tumors were lymphangioma and non-Hodgkin lymphoma, respectively. The majority of benign tumors were found in children younger than 2 years old (P=0.007), but there was no age predilection for malignant tumors. Conclusion. According to our results, benign tumors were more prevalent than malignant ones. Although a low rate of benign tumors in males shows that more attention should be paid to the early diagnosis of head and neck tumors.