Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors.

The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma.

Practical Acoustic Energy-Based Estimation of Inhalation Flow Rate for Asthma Monitoring.

The estimation of inhalation flow rate (IFR) using acoustic devices has recently received attention. While existing work often assumes that the microphone is placed at a fixed distance from the acoustic device, this assumption does not hold in real settings. This leads to poor estimation of the IFR since the received acoustic energy varies significantly with the distance.