Improving Feature Selection for Infant Mortality Risk Assessment via Binary Multi-Objective Cheetah Optimization

Infant mortality is a pivotal indicator of community health and socioeconomic conditions. Despite global advancements in healthcare and significant reductions in infant mortality rates, substantial disparities persist, particularly in underserved populations. This research aims to tackle these disparities by enhancing the predictive accuracy of public health interventions. We utilize advanced feature selection techniques to identify critical predictors of infant survival, thereby supporting the development of targeted and effective health policies and practices.

We introduce an enhanced Binary Multi-Objective Cheetah Optimization algorithm (BMOCO), specifically designed for feature selection in extensive medical datasets. The suggested method focuses on optimizing eight S-shaped and V-shaped transfer functions to refine the conversion of continuous position vectors into binary form, ensuring precise feature selection and robust model performance.

The BMOCO method demonstrates superior accuracy and effectiveness in feature selection compared to traditional evolutionary optimization algorithms such as MOGA (Multi-Objective Genetic Algorithm), MOALO (Multi-Objective Ant Lion Optimizer), NSGA-II (Non-dominated Sorting Genetic Algorithm II), and MOQBHHO (Multi-Objective Quadratic Binary Harris Hawk Optimization). Applied to U.S. infant birth data, our approach achieves an average classification accuracy of 99.54%. Critical factors impacting infant mortality identified include maternal literacy, prenatal care frequency, and pre-existing maternal conditions, such as diabetes, smoking during pregnancy, body mass index, infant birth weight, and breastfeeding practices. These findings indicate that the optimized BMOCO model provides interpretable and data-driven insights that align with established clinical evidence.

The results underscore the effectiveness of advanced machine learning techniques in uncovering significant health predictors.

The proposed BMOCO algorithm offers a robust and interpretable tool for health professionals to enhance predictive models, facilitating targeted interventions to reduce infant mortality rates and improve public health outcomes.