Asthma Disease Prediction using Machine Learning Classifiers

Asthma is a chronic inflammatory disease that affects parts of the respiratory system of millions of people. It includes symptoms such as breathlessness and wheeze with the potential of life threatening attacks. It is a disease that can be anticipated early and if this is done, it will greatly enhance the ability to manage the disease whilst avoiding further problems. The proposed paper integrates numerous works into a review of a wealth of literature relating to asthma prediction models and methods. Advanced data mining techniques and machine learning algorithms are used to analyses health records to identify risk factors for accurate prediction of asthma.In addition, the models such as Random Forest, Support Vector Machines, XG Boost, and Logistic Regression, are also classified as these models. These models possess been applied on different datasets and thus display great predictive accuracy. Family history of asthma, poverty ratio and early life appearance of wheezing were also pointed out to be important risk factors apart from model performance. Incorporation of such models with a national database can assist in early detection, targeted treatment and control of asthma, especially in those at high risk like children and adolescents. The proposed work will be carried out using Google Colab.