Detection of depression and anxiety in the perinatal period using Marine Predators Algorithm and kNN

Undiagnosed prenatal anxiety and depression have the potential to worsen and have an adverse effect on both the mother and the infant. Although the diagnosis is made by specialist doctors, it is unclear which parameters are more effective. Especially in medicine, it is crucial to diagnose disease with high accuracy. For this reason, in this study, a questionnaire study was first conducted on pregnant women, and real original data were collected. Then, the Marine Predators Algorithm (MPA), one of the current metaheuristic algorithms inspired by nature, was combined with K-Nearest Neighbors (kNN) to determine high-priority features in the collected data. As a result, five of the 147 features selected by the proposed method were determined as high priority and approved by the doctors. In addition, the proposed method is compared with the Chi-square method, which is one of the filter-based feature selection methods. Thanks to the proposed feature selection method based on MPA and kNN, it has been observed that the classification gives more successful results in a shorter time with 98.11% success, and the model supports the diagnosis stage of the doctors. •Real patient data for diagnosis of anxiety and depression in perinatal period.•Feature selection for high-dimensional data.•Improved accuracy with MPA+kNN hybrid method.•Outperforming alternative methods.•Clinical relevance for healthcare professionals.