A Review of Pre-Pregnancy Health Risk Assessment in Women

Pre-pregnancy health assessment is crucial for identifying and managing risk factors affecting maternal and fetal health. This paper reviews traditional approaches like routine medical exams, lifestyle evaluations, and basic health metrics, noting their limitations in precision and personalization, especially for diverse populations. To bridge these gaps, the paper explores advanced methods such as artificial intelligence (AI), soft computing, genetic profiling, and wearable technologies. AI-driven models and techniques like fuzzy logic show potential in improving predictive accuracy, while wearable devices enable real-time health monitoring for proactive care. The paper underscores the need for a holistic, personalized risk assessment framework integrating various health metrics, advocating for interdisciplinary collaboration to enhance outcomes in maternal and infant health, and providing a foundation for researchers and clinicians to advance pre-pregnancy care.