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    A machine learning approach to differentiate between acute asthma and bronchitis in preschool children
    (Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü, 2022) Salih, Waleed Hameed Salih; Koyuncu, Hakan
    Among the common diseases that affects the lower respiratory tract infections (LRTI) of children around the world are acute asthma and bronchitis, it often occurs in preschool age and there are 12 clinical features overlapping between the two diseases, the most common of which are coughing, wheezing, runny nose, and shortness of breath. therefore, most people do not distinguish between two diseases, it is necessary to visit a specialist doctor to diagnose the cases and to receive the appropriate treatment for each case , due of the large number of cases that increase during weather fluctuations, such as high and low temperatures and environmental pollution such as fumes smoke and dust, etc., which need an accurate diagnosis, many junior doctors working in emergency halls face difficulties in diagnosing cases and the differentiation between the two diseases, so these doctors’ resort to the consulting doctor to obtain a diagnosis that differentiates between the two diseases. In this study, we presented 3 machine learning models ( K - NN , Decision Tree , MLP) and 2 deep learning models ( 1D-CNN, LSTM ), where we trained this models on a text dataset consisting of 512 real cases that collected by the consultant paediatrician at Fallujah Teaching Hospital for Women and Children in Iraq during four months started in march 2022 to June 2022 and after using all modern methods the final results showed that the 1D-CNN outperformed the rest of the models with an accuracy of (99.3506) and ROC – AUC ( 99.32 ) which was chosen as a binary classifier to this study .