Machine Learning Based Delivery Date Prediction For Child Birth

Health systems bear a crucial responsibility for the well-being of individuals across their entire lifespan. Accurate prediction of the delivery date for childbirth is of paramount importance for both the expectant mother and her infant. It plays a vital role in ensuring the safety and health of both parties. A miscalculated decision in this regard not only increases the risks for the mother's life but also poses potential harm to the newborn. To address this issue and mitigate associated risks, we have developed a prediction model based on machine learning techniques. In this research study, we utilized the Ethiopian Demographic and Health Survey (EDHS) dataset to predict the delivery date for childbirth and create our model. We follow experimental research design methodology as a result we employed an experiment regression-based machine learning algorithms, including Linear Regression, Multilayer Perceptron, and Random Forest, to develop the predictive model. Once the model was constructed, we evaluated its performance using well established evaluation metrics, such as mean absolute error, mean squared error, and
${\boldsymbol R}^{2}$
score. Upon careful examination of the evaluation results, we determined that the
${\boldsymbol R}^{2}$
score served as a reliable metric to assess the performance of our models. The obtained
${\boldsymbol R}^{2}$
scores were 0.35 for Linear Regression, -25.1 for Multilayer Perceptron, and 0.67 for Random Forest. Based on these scores, it became evident that the Random Forest algorithm exhibited superior performance compared to the other algorithms.