A Neural Network Approach for Risk Assessment of Asthma Disease

The work in this paper illustrates the deployment of neural network as a machine learning approach for the risk assessment of the asthma disease as indicated by a significant pulmonary function parameter called Tiffeneau-Pinelli index used in the prognosis of obstructive respiratory diseases such as Asthma. The approach is trained and tested on samples taken from SPIROLA dataset. We deploy different neural network types for the prediction of the index and evaluate the performance with respect to their predictive capability, thereby concluding that a few of the neural network types can be relied on for the effective prediction of the disease. The transformed features used in the input features set have proved to yield good prediction results thereby avoiding the need to employ individual raw features for the prediction process.