Aim: To examine the suitability of Artificial Neural Network (ANN) in predicting infant mortality and
compare its performance with Logistic Regression (LR) model.
Study Design: A cross-sectional population based study was conducted. The 2013 Nigeria Demographic
Health Survey (NDHS) data were used.
Place and Duration of Study: The study was conducted in Nigeria and the fieldwork was carried out
from February 15, 2013, to May 31, 2013.
Methodology: Data were partitioned into training and testing sets with ratio 7:3. Logistic and ANN
models were fitted on the training set and were validated using the testing sample. Akaike Information
Criterion (AIC) and Area under curve (AUC) were used as criteria for comparing the two models. The
discriminative ability was measured using sensitivity and specificity. Variable importance analysis was
also conducted to determine the magnitude of contribution of each predictor to the outcome.
Results: The sensitivity of the classification model was 67% and 76% for the LR and the ANN models
respectively. Specificity of the prediction was 94% for the two models. Overall accuracy was
approximately 81% and 83% for LR and ANN respectively. The AIC values were 9462 and 9614 for
ANN model and LR model respectively. Area under curve was 0.621 and 0.637 for the LR model and the
ANN model respectively. The variable importance analysis showed that preceding birth interval less than
24 months and not receiving tetanus toxoid injection during pregnancy had the highest positive
contribution to infant mortality.
Conclusion: The artificial neural network model had a higher sensitivity than the logistic regression
model. Preceding birth interval of less than 24 months and non-reception of tetanus toxoid injection by
mothers’ during pregnancy were important predictors of infant mortality in Nigeria.