Risk Analysis and Classification of Maternal Data to Enhance Women’s Health

Reducing the number of redundant Caesarean sections in tertiary care centers is essential for enhancing the health outcomes of both mothers and newborns. These institutions need to weigh the advantages of surgical procedures against the encouragement of natural childbirth when it is medically suitable. Even though a C-section is a valuable intervention to save both the mother and the child from certain life-threatening conditions during pregnancy, excessive sections pose serious health hazards and lead to huge financial losses. It is a mixed-approach that involves the analysis of quantitative data along with evidence-based interventions. The convergence of machine learning based technologies in gynecology has resulted in the development of innovative digital health that allow easy and accurate patient risk stratification in early pregnancy, labor and delivery and care of newborn. The results reflect that maternal weight and age are amongst the two most influential factors in C-section delivery with increased risks for heavier and older mothers. The results of empirical study recommended targeted weight management interventions, personalized care of older mothers, and better labor practices as strong ways to reduce unnecessary C-sections, improving maternal and neonatal health outcomes