Background:Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy.Objectives:In order to increase the classification accuracy of AIRS, this study introduces a new hybrid system that incorporates a support vector machine into AIRS for diagnosing tuberculosis.Patients and Methods:Patient epacris reports obtained from the Pasteur laboratory of Iran were used as the benchmark data set, with the sample size of 175 (114 positive samples for TB and 60 samples in the negative group). The strategy of this study was to ensure representativeness, thus it was important to have an adequate number of instances for both TB and non-TB cases. The classification performance was measured through 10-fold cross-validation, Root Mean Squared Error (RMSE), sensitivity and specificity, Youden’s Index, and Area Under the Curve (AUC). Statistical analysis was done using the Waikato Environment for Knowledge Analysis (WEKA), a machine learning program for windows.Results:With an accuracy of 100%, sensitivity of 100%, specificity of 100%, Youden’s Index of 1, Area Under the Curve of 1, and RMSE of 0, the proposed method was able to successfully classify tuberculosis patients.Conclusions:There have been many researches that aimed at diagnosing tuberculosis faster and more accurately. Our results described a model for diagnosing tuberculosis with 100% sensitivity and 100% specificity. This model can be used as an additional tool for experts in medicine to diagnose TBC more accurately and quickly.
Mining itemsets plays an important role in all fields of data mining research, such as: association rules, clustering, and classification. Mining all frequent itemsets leads to a massive number of itemsets. This problem can be reduced by finding maximal frequent itemsets (MFI). In this paper, a new method for mining all MFI based on graph theory, is proposed. In the presented method, first, a square matrix corresponding to the transaction elements of database is formed. Then the graph of this matrix is considered and its maximal complete subgraphs (maximal cliques) which are in one-to-one correspondence with MFI are found. Experimental results verify the advantages of the proposed method including: efficiency, simplicity, accuracy, reasonable time and memory space. Moreover, the presented method has good performance in the case of large databases.
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