Regression based machine learning models for forecasting preterm birth cases

Abstract

Preterm birth (PTB) is a significant health issue, leading to neonatal mortality and morbidity worldwide. However, effective forecasting model may assist to reduce the rate of PTB cases. In this study, three different regression-based forecasting models namely simple linear regression (SLR), multi-variate linear regression (MLR) and standard ARIMA are introduced over the data collected (during 01.01.2017-01.05.2022) from Bokaro hospital of India for medium-term forecasting of PTB cases. The models are trained and validated over parts of the dataset. On the basis of forecasting-accuracy (%age), RMSE and R-square values achieved over the validation-period, the proposed MLR model is the more accurate one to forecast PTB cases.