Predicting Antenatal Depression during 3rd Trimester: A Machine Learning Approach with Feature Selection and TOPSIS Ranking

This study aimed to predict antenatal depression in erythroid women during their 3rd trimester. We investigated prediction using four feature selection methods: Extra-tree classifier, Fisher score, and PCA. We also merged the common features of the Extra-tree classifier and Fisher score and applied them to predicting antenatal depression in the 3rd trimester of pregnancy. We gathered data from 62 women and their corresponding 18 attributes and evaluated them using the Hamilton depression rating scale (HAM-D). Seven ML models were implemented to predict antenatal depression, including k-nearest neighbors, Support vector machine, random forest, decision tree, bagging classifier, multi-layer perception, and naïve Bayes. Therefore, the trained models were evaluated using various metrics, including accuracy, sensitivity, specificity, precision, F1 score, FNR, FPR, and area under the receiver operating characteristic curve. Ultimately all models were prioritized using TOPSIS with different feature selection methods, and the best model was found to be DT without implementing any feature selection. The results of this study show the most important factors in predicting depression in the 3rd trimester of pregnancy.