Journal of Infectious Diseases & Preventive Medicine
Open Access

Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Publications

• Research Article   
  Application of Data Mining Techniques in Tuberculosis (TB) Diagnosis: A Comparison of Multilayer Perceptron Neural Network (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS) Efficiency
  Author(s): Azamossadat Hosseini*, Hamid Moghaddasi, Reza Rabiei and Sara Mohebi Mushaei
  
  Background: Data mining techniques for disease diagnosis help the prediction and control of various diseases, including Tuberculosis (TB). This study aimed to compare the efficiency of two main models of TB diagnosis: MLP (Multilayer Perceptron Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference System) to find out which data- mining-based model is more efficient in detecting tuberculosis. Materials and methods: In this analytical study, database used was for inpatients in a specialized hospital for lung and respiratory diseases. The database included 1159 records, of which 599 records belonged to TB infected patients and 560 records to non-infected patients. With help of 13 factors effective on diagnosis of the disease and using the set of TB records, the two models of MLP and ANFIS were tested and evaluated. Finally, .. View More»
  

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