Predicting Mothers with Postpartum Depression using Machine Learning Approaches

Prabhashwaree, T. H. K. R.; Wagarachchi, N. Mihirini

doi:10.1109/scse56529.2022.9905186

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…The existing deep learning models MLP [16], XRT [21], DT [23], LR [25] and XGB [26] can detect the PPD from the dataset, but they do not effectively optimize the parameters resulting in high computational complexity. In contrast, the proposed OPOMLP model specifically addresses this limitation and efficiently learns this information, resulting in high PPD detection ability.…”

Section: Discussionmentioning

confidence: 99%

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Predicting Postpartum Depression with Aid of Social Media Texts Using Optimized Machine Learning Model

2024

IJIES

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Post-Partum Depression (PPD) is a significant medical condition that occurs in some women after childbirth as a consequence of physiological behavioral and mental alterations. The complicated symptoms of this condition make it difficult to diagnose and differentiate from other conditions. Timely detection and diagnosis of PPD are crucial for controlling morality rates and ensuring effective treatment. Various Machine Learning (ML) models was developed to predict PPD based on the patients demographic status, mental health history and vital signs. But, additional psychological attributes are needed for predicting mental status and identifying individuals with PPD risk. Also, costeffective and innovative methods are needed to identify individuals with PPD and detect potential development tendencies. Hence, in this paper, Osprey Parameter Optimized MLP (OPOMLP) is developed to address the above issues for efficient PPD. Initially, the Application Programming Interface (API) function of online social network like Twitter (tweets) and Instagram (comments) are used to collect the data posted by health care professionals. Then, Natural Language Processing (NLP) is utilized to pre-process the collected data \and extract relevant text of Twitter users to estimate PPD phases. The additional psychological attributes like mental health and behavioural changes attributes of women are extracted using Linguistic Inquiry Word Count (LIWC) and Latent Semantic Analysis (LSA) methods. Next, MLP network is trained using the extracted attributes along with the attributes of demographic status, mental health history and vital signs. Since, the parameters of MLP were not optimized properly which leads to computational complexities in the PPD prediction. So, the weights initialization and the hyper-parameters of MLP is optimized simultaneously by using an Osprey Optimization Algorithm (OOA). OOA is a metaheuristic optimization algorithm derived from osprey bird hunting behavior which aims to find the global optimum solutions and reduces the complex optimization issues. The relationship between hyperparameters and classification performance will identify an optimal hyperparameter space regions for optimal classification with less computational time and resources. Finally, the OPOMLP is employed for the final prediction of PPD. The test outcomes reveal that the OPOMLP model achieves an accuracy of 96.12% on the collected dataset compared to the classical PPD detection models.

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Section: Discussionmentioning

confidence: 99%

“…ML model predicted [26] women who are suffering with PPD. The data, including mother's relatives and information-related positions was preprocessed and standardized using Min-Max normalization.…”

Section: Literature Surveymentioning

confidence: 99%

Predicting Postpartum Depression with Aid of Social Media Texts Using Optimized Machine Learning Model

2024

IJIES

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“…The proposed methodology encompasses utilizing an RF method to classify depression based on the estimation of scores. It has been suggested that a new method could be used to detect depression, which uses linguistic signals and extracts content from responses from individuals based on the language they use 15 .…”

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%

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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Performance Comparison of Machine Learning Techniques for Early Detection of Postpartum Depression Using PRAMS Dataset

Paul,

Pragada,

Murthy

et al. 2023

2023 IEEE 15th International Conference on Computational Intelligence and Communication Networks (CICN)

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Predicting Mothers with Postpartum Depression using Machine Learning Approaches

Cited by 3 publications

References 18 publications

Predicting Postpartum Depression with Aid of Social Media Texts Using Optimized Machine Learning Model

Predicting Postpartum Depression with Aid of Social Media Texts Using Optimized Machine Learning Model

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

Performance Comparison of Machine Learning Techniques for Early Detection of Postpartum Depression Using PRAMS Dataset

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