ENHANCING MATERNAL PSYCHOLOGICAL HEALTH DURING PREGNANCY THROUGH E-HEALTH INFORMATICS

This research investigates the enhancement of maternal psychological health during pregnancy through the integration of E-health informatics and quantum photonics for security. The study reviews interventions and outcomes related to E-health technologies in maternal mental health, exploring intersections with quantum photonics for enhanced security measures. The primary objective is to assess the efficacy of E-health interventions in promoting maternal well-being, with an innovative link to quantum photonics for security. The study encompasses algorithmic approaches and predictive modeling, exploring potential synergy with quantum photonics for securing healthcare data. Using a systematic approach, publicly available datasets, including Kaggle, are employed. Data preprocessing addresses missing values, encodes categorical variables, and scales features. Eight machine learning algorithms are deployed for predictive modeling. Evaluation reveals distinctive performances among algorithms, with Random Forest leading in accuracy, precision, and recall. Quantum photonics integration is explored, laying the groundwork for securing health data. In conclusion, the study highlights Random Forest's potential in predicting psychological health risks, and integrating quantum photonics introduces innovative security measures. Future directions include refining predictive pathways, exploring additional features, and validating with diverse datasets. Advanced mathematical calculations, algorithmic enhancements, and deeper integration of quantum photonics are suggested to contribute to evolving digital health interventions and innovative studies in health prediction and data security.