Application of Mamdani Fuzzy Logic in Identifying Postpartum Depression Risk
Abstract
Introduction: Postpartum depression (PPD) is a common psychological disorder affecting mothers after childbirth, often underdiagnosed due to the subjective nature of its symptoms. Early detection is crucial to prevent adverse effects on maternal and child health. This study aims to develop an early detection system for PPD risk using Mamdani fuzzy logic, which is well-suited to handle vague and imprecise symptom data. Methods: A fuzzy inference system was designed using the Mamdani method to classify PPD risk into Low, Medium, and High categories. The system was built upon a dataset of 1503 questionnaire responses sourced from Kaggle. Subjective symptoms such as sadness, irritability, sleep disturbances, and bonding difficulties were mapped into fuzzy membership functions. A total of 243 fuzzy rules were defined to reflect realistic combinations of symptoms. The system was implemented and validated in both Python and LabVIEW environments. Results: Experimental validation using 10 test inputs showed consistent results between the two platforms, with a deviation of less than ±1%. This consistency confirms the reliability of the fuzzy logic model in interpreting subjective symptom data. The system demonstrated strong potential for classifying PPD risk based on nuanced input variables. Conclusions: The Mamdani fuzzy logic system offers a reliable and flexible tool for assessing postpartum depression risk. By effectively interpreting ambiguous symptoms, it supports healthcare professionals in identifying at-risk individuals for early intervention. Future enhancements should include expanding the dataset and refining the rule base for broader applicability and improved accuracy.
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