Detecting Postpartum Depression Stages in New Mothers: A Comparative Study of Novel LSTM-CNN vs. Random Forest

Abstract

A Novel long-short term memory with convolutional neural networks (LSTM-CNN) is used to predict
postpartum depression and compared it with Random Forest (RF) Algorithm. Materials and Methods: For this
research two groups were taken: The Novel Long-Short Term Memory with Convolutional Neural Networks
(LSTM-CNN) and for comparison the Random Forest (RF) Algorithm was considered. After careful
consideration each with a sample size of 20 to help in this research. Results: The outcomes of the study are
shown in the following table (LSTM-CNN). The mean accuracy of the LSTM -CNN is 77.75% and the
Random Forest (RF) Algorithm model is 72.12%, respectively. The significance of the Independent sample
t-test is evident with a p-value of 0.04 (p < 0.05), underscoring the statistical significance of the comparison
between the LSTM-CNN model and the Random Forest algorithm in the study. Conclusion: The LSTM-CNN
technique outperformed the Random Forest (RF) Algorithm and other machine learning algorithms in terms
of accuracy, and deep learning algorithms have generally showed promise in the prediction of Postpartum
depression.