Post-Partum Depression at the Regional Special Hospital of South Sulawesi Province

Saad, Rahmiyani; Leli, Leli

doi:10.47616/jamrmhss.v3i1.252

Cited by 1 publication

(3 citation statements)

References 8 publications

(14 reference statements)

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“…Various approaches 19 , 20 are employed for the identification of depression and the categorization of textual, visual, and auditory characteristics. Based on the findings reported in the article 10 , the f1-score is determined to be 0.84%, while the precision is measured at 0.82%.…”

Section: Literature Reviewmentioning

confidence: 99%

“… Detection rate is poor. 19 Genetic Algorithm Diverse Medical care dataset Found PPDD-prone genetic markers. Limited to genetics; ethical concerns.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The above experimental results prove that the proposed model performed better than existing models. Figures 13,14,15,16,17,18,19,20,21,22 and 23 presents the visualization of simulation results for 100 Epochs for existing and proposed models.…”

Section: Simulationmentioning

confidence: 99%

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Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model

Lilhore,

Dalal,

Varshney

et al. 2024

Sci Rep

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Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and ‘speech records’ of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

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Section: Literature Reviewmentioning

confidence: 99%

“… Detection rate is poor. 19 Genetic Algorithm Diverse Medical care dataset Found PPDD-prone genetic markers. Limited to genetics; ethical concerns.…”

Section: Literature Reviewmentioning

confidence: 99%