A hybrid approach for forecasting peak expiratory flow rate in asthma patients using combined linear regression and random forest model.

Asthma is a frequent and long-lasting disorder associated with airway inflammation. The disease severity may lead to serious health concerns and even mortality. In this work, we propose a novel hybrid approach using machine learning models and similarity measurement technique with the aim of precise peak expiratory flow rate (PEFR) estimation for asthma trigger assessment. The random forest model was first utilized to classify the PEFR percentile zones on unseen data. Then, two linear regression models following thresholds of <50% and >=50% were hypothesized and trained to achieve better outcomes than a single standalone model. Hence, the input is diverted to the relevant model for prediction based on classification results. Furthermore, a string-matching technique has been proposed to obtain reference outcomes in addition to yesterday's PEFR. Finally, a supplementary linear regression model is used to make predictions based on input of two prediction values and one PEFR value from the previous day. The proposed model is evaluated on a dataset of 25 patients, each with 2 to 3 months of recordings, on average. The findings showed reduced mean and random absolute error of 27.064 L/min and 1.34%, respectively, using the suggested model, compared to 79.794 L/min and 4.42% error rates by the standalone linear regression model on five-fold cross-validation. The outcome indicates that the proposed hybrid algorithm accurately predicts asthma-trigger events.