Spirometry Data Classification Using Self Organizing Feature Map Algorithm
نویسندگان
چکیده
In this work the classification of Force Expiratory volume in 1 second (FEV 1) in pulmonary function test is carried out using Spirometer and Self Organizing Feature Map Algorithm. Spirometry data are measure with flow volume spirometer from subject (N=100 including Noramal, and Abnormal) using standard data acquisition protocol. The acquire data are then used to classify FEV1. Self Organizing Map was used to classify the values of FEV1 into Normal, Obstructive and Restrictive. The Spirometry data was statistically analyzed for neural network. The FEV1 parameters were presented as inputs to Self Organizing map algorithm. The self organize map classified normal and abnormal classes, abnormal class again classified into Obstructive and restrictive classes. The result shows the Accuracy, Sensitivity and Specificity of Self Organizing Map algorithm.
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