| dc.description.abstract | Postpartum depression is a medical condition which affects many mothers. The
condition exposes the mother and the newborn baby to illnesses that can lead to death.
Management of the condition requires heavy expenditure incurred by the family,
government, and stakeholders. The condition is also a source of many social problems.
Manual systems are currently used to predict the condition which is slow and
inconsistent. Machine learning technology which has reliably been used in prediction
modelling in other domains can also be employed to build a model to predict mothers
at risk of postpartum depression during pregnancy for primary prevention. In this
study, perinatal records were collected from 324 mothers attending postnatal healthcare
clinics at the Coast General Teaching and Referral Hospital and Tudor Sub-County
Hospital in Mombasa County. The filter feature in WEKA was used to split the data
into 70% and 30% for model training and testing respectively. Models were built on
WEKA machine learning platform using logistic regression, support vector machine,
extremely randomized trees, random forest and adaptive boosting algorithms which
were identified from literature. A positive case of postpartum depression was defined
as diagnosis or treatment of major depression within one year after delivery. Random
forest model produced the best performance with a receiver operating characteristic
(ROC) curve area of 0.863867 which is comparable within the bracket of high
performing models. With this level of performance, the model can be used by
healthcare staff to make quick and consistent prediction for early mitigation measures.
Further research could be done with more data collected from other counties in Kenya | en_US |
