Multilayer perceptron and support vector regression models for feline parturition date prediction.

A crucial challenge in feline obstetric care is the accurate prediction of the parturition date during late pregnancy. The classic simple linear regression (SLR) model, which employed the fetal biparietal diameter (BPD) as the single input feature, was frequently applied for such prediction with limited accuracy. Since Multilayer Perceptron (MLP) and Support Vector Regression (SVR) are now two of the most potent scientific regression models, this study, for the first time, introduced such models as the new promising tools for feline parturition date prediction. The following features were candidate inputs for our models: biparietal diameter (BPD), litter size, and maternal weight. We observed and compared the performance results for each model. As the best-performed model, MLP delivered the highest coefficient score (0.972 ± 0.006), lowest mean absolute error score (1.110 ± 0.060), and lowest mean squared error score (1.540 ± 0.141), respectively. For the first time in this study, BPD, litter size, and maternal weight were considered the essential features for the innovative MLP and SVR modeling. With the optimized model parameters and the described analytical platform, further verification of these advanced models in feline obstetric practices is feasible.

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

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.

Prenatal diagnosis of foetal hydrocephalus and suspected X-linked recessive inheritance of cleft lip in a Chihuahua.

A 3.5-year-old, 2.9 kg, multiparous Chihuahua presented with abdominal distension; pregnancy was diagnosed. On Day 7 before parturition, prenatal sonograms showed anechoic bilateral dilated cerebral lateral ventricles, suggesting fluid-filled regions (ventriculomegaly) in one foetus. A Caesarean section was performed and the male newborn had an abnormally enlarged dome-shaped head and a cleft lip, and died 6 days after birth. According to the family pedigree, the X-linked recessive inheritance of an orofacial cleft from the unaffected mother was suggested. This report clearly demonstrates that canine foetal ventriculomegaly (hydrocephalus) can be diagnosed in utero. For dog breeds predisposed to congenital ventriculomegaly, early detection is important for the prediction of perinatal survival and adequate supportive care can be applied at delivery.