Support vector regression algorithm modeling to predict the parturition date of small - to medium-sized dogs using maternal weight and fetal biparietal diameter

Abstract

Background and Aim: Fetal biparietal diameter (BPD) is a feasible parameter to predict canine parturition date due to its inverted correlation with days before parturition (DBP). Although such a relationship is generally described using a simple linear regression (SLR) model, the imprecision of this model in predicting the parturition date in small- to medium-sized dogs is a common problem among veterinarian practitioners. Support vector regression (SVR) is a useful machine learning model for prediction. This study aimed to compare the accuracy of SVR with that of SLR in predicting DBP. Materials and Methods: After measuring 101 BPDs in 35 small- to medium-sized pregnant bitches, we fitted the data to the routine SLR model and the SVR model using three different kernel functions, radial basis function SVR, linear SVR, and polynomial SVR. The predicted DBP acquired from each model was further utilized for calculating the coefficient of determination (R2), mean absolute error, and mean squared error scores for determining the prediction accuracy. Results: All SVR models were more accurate than the SLR model at predicting DBP. The linear and polynomial SVRs were identified as the two most accurate models (p<0.01). Conclusion: With available machine learning software, linear and polynomial SVRs can be applied to predicting DBP in small- to medium-sized pregnant bitches.

Keywords

• biparietal diameter
• dog size
• prediction accuracy
• support vector regression