Predicting difficulties in Mask Ventilation using Machine Learning techniques

The oxygen is to be supplied constantly through the mask to a patient in the Operation Theater (OT) when performing an operation. Any interruption in oxygen or air supply to the patient may lead to severe bodily damage or even death of the patient. The mask ventilation can be categorized into 3 levels namely easy, difficult and impossible mask ventilation. An expert anesthesiologist can accurately predict the difficulties in mask ventilation. Currently, expert anesthesiologists use their experience to manually analyze the patient features and predict the difficulties in mask ventilation. So authors have implemented a software solution by applying machine learning algorithms to predict the difficulties in mask ventilation. Authors have identified twelve physical parameters of the patient that are significant in predicting the difficulties in mask ventilation. The representative patient data collected from hospital and the knowledge of experienced anesthesiologist is used to synthesize the data set. The data set is mined using machine learning algorithms namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes and k-Nearest Neighbors. Logistic Regression algorithm is proved to be better in predicting the difficulties in Mask Ventilation.