  Analysis of child mortality is crucial as it pertains to the policy and
programs of a country. The early assessment of patterns and trends in causes of
child mortality help decision-makers assess needs, prioritize interventions,
and monitor progress. Post-birth factors of the child, such as real-time
clinical data, health data of the child, etc. are frequently used in child
mortality studies. However, in the early assessment of child mortality,
pre-birth factors would be more practical and beneficial than the post-birth
factors. This study aims at incorporating pre-birth factors, such as birth
history, maternal history, reproduction history, socioeconomic condition, etc.
for classifying child mortality. To assess the relative importance of the
features, Information Gain (IG) attribute evaluator is employed. For
classifying child mortality, four machine learning algorithms are evaluated.
Results show that the proposed approach achieved an AUC score of 0.947 in
classifying child mortality which outperformed the clinical standards. In terms
of accuracy, precision, recall, and f-1 score, the results are also notable and
uniform. In developing countries like Bangladesh, the early assessment of child
mortality using pre-birth factors would be effective and feasible as it avoids
the uncertainty of the post-birth factors.
