FEMALE INFANT BIRTH RATE PREDICTION MODEL

This study focused on the development of a Female Infant Birth Rate Prediction Model aimed at improving the accuracy of forecasting female infant birth rates using statistical and computational techniques. The increasing importance of population planning, healthcare management, and demographic analysis necessitates reliable prediction models capable of assisting policymakers and healthcare institutions in making informed decisions. The study utilized historical birth data obtained from relevant health institutions and demographic databases. Various predictive variables such as maternal age, fertility rate, healthcare access, socio-economic factors, and environmental conditions were considered in developing the model. A quantitative research approach was adopted, and data analysis was carried out using statistical and machine learning techniques to identify patterns and relationships among the variables. The prediction model was developed using regression analysis and other suitable predictive algorithms to estimate future female infant birth rates. The performance of the model was evaluated using standard accuracy measures such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and coefficient of determination (R²). The findings indicated that the developed model achieved a satisfactory level of prediction accuracy and can serve as a useful tool for demographic forecasting and healthcare planning. The study concluded that predictive modeling techniques are effective in estimating female infant birth rates and can significantly support government agencies, healthcare organizations, and researchers in policy formulation and resource allocation. The study recommended continuous updating of demographic databases and the integration of advanced machine learning techniques to further improve prediction accuracy and reliability.