الفهرس | Only 14 pages are availabe for public view |
Abstract Mortality and morbidity from respiratory disorders are among the most frequent causes of death and morbidity worldwide. Study after study has demonstrated that computer-assisted learning tools improve self-directed learning, as well as improving problemsolving and forecasting skills Only a small number of respiratory medicines have been produced so far, though. Thus, in this study a framework has been developed for recognizing respiratory illnesses using Data Mining Techniques (DMT) on clinical info. In order to evaluate the prediction model, the framework consists of three key phases: preprocessing, data mining, and evaluation. Feature selection algorithm has been applied on the clinical data. Mostly a 20 percent sample was used to test the model. The remaining 80 percent was used to train a classifier. The resulting classes were about the diagnosis of the respiratory system disease, i.e., Normal Spirometry, Moderate Restriction, Mild Restriction and Severe Restriction. The decision tree has 83.3% sensitivity, 92.1% specificity, the Classifier’s overall accuracy is 90.9%. As part of the reasoning phase, a tool has been developed to execute the prediction process. |