Prediction of Postpartum Depression With Dataset Using Hybrid Data Mining Classification Technique
DOI:
https://doi.org/10.22399/ijcesen.750Keywords:
PPD, DM, ANN, SVM, Hybrid ClassifierAbstract
Postpartum Depression is a condition or a state which usually affects the woman immediately after child birth. The birth of a baby not only brings delighted emotions such as excitement, but also fear and anxiety which may sometimes lead to depression. It is a period of physical, emotional and behavioral changes that happen in some woman immediately after the delivery. Apart from the chemical changes, there are many factors which affect a woman during and after pregnancy period. If PPD is not identified and treated at the earlier stages, it may lead to serious issues for mother and child. It is therefore of vital importance to sift through the woman at any early stage to prevent any consequences. The objective of this study is to find out the presence of PPD without getting worse. Data mining plays an important role in the health care industry with successful outcome. It helps to find out hidden patterns, trends and anomalies from large dataset to make the predictions. The proposed system is a combined classification technique for the prediction of postpartum depression that uses Support vector machine, Artificial Neural Network and Hybrid classifier algorithm to produce the best result.
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