Enhancing the precision of male fertility diagnostics through bio inspired optimization techniques

Infertility is a growing concern in today's technologically driven and mechanized world, with male related factors contributing to nearly half of all cases yet often remaining under diagnosed due to societal misconceptions and stigma. Prolonged sedentary behaviour, environmental exposures, and psychosocial stress further exacerbate reproductive health disorders. This study presents a hybrid diagnostic framework that combines a multilayer feedforward neural network with a nature-inspired ant colony optimization algorithm, integrating adaptive parameter tuning through ant foraging behaviour to enhance predictive accuracy and overcome the limitations of conventional gradient based methods. Unlike conventional fertility diagnostic approaches, this hybrid strategy demonstrates improved reliability, generalizability and efficiency. The model was evaluated on a publicly available dataset of 100 clinically profiled male fertility cases representing diverse lifestyle and environmental risk factors, with performance assessed on unseen samples. Remarkably, it achieved 99% classification accuracy, 100% sensitivity, and an ultra-low computational time of just 0.00006 seconds, highlighting its efficiency and real-time applicability. Clinical interpretability is achieved via feature-importance analysis, emphasizing key contributory factors such as sedentary habits and environmental exposures, thereby enabling healthcare professionals to readily understand and act upon the predictions. This cost effective, time efficient system has the potential to reduce diagnostic burden, enable early detection, and support personalized treatment planning, illustrating the effective synergy between machine learning and bio-inspired optimization in advancing male reproductive health diagnostics.