| Popis: |
Asthma is one of the common chronic diseases in children affecting more than 6 million, which is identified by inflammation in the airways which causes irritation in airflow. This paper helps to predict asthma-affected people using data mining classification techniques. Generally, asthma can be identified using certain types of breathing tests, and they are FEV1/FEC, FEF. In this paper, predictions are given based on the symptoms of the patient. Performing the tests on children could be difficult, so we use predictions on symptoms. Mostly, machine learning algorithms are there to predict asthma such as support vector machine, artificial neural networks, k-nearest neighborhood algorithms, AdaBoost and random forest algorithms. It comprises the analysis of various classification techniques, an asthma prediction. It analyzes the classification techniques used to identify the disease based on accuracy level. |
