Neonatal sepsis is a critical pathology that particularly affects the neonates in intensive care, especially if they are preterm and low birth weight, with an incidence varying between 1and 40% according to the onset (early or late) of the disease. Prompt diagnostic and therapeutic interventions could reduce the high percentage of mortality that characterises this pathology, especially in the premature and low weight neonates. The HeRO score analyses the heart rate variability and represents the risk of contracting sepsis because of the hospitalization in the neonatal intensive care unit up to 24 h before the clinical signs. However, it has been demonstrated that the HeRO score can produce many false-positive cases, thus leading to the start of unnecessary antibiotic therapy. In this work, the authors propose an optimised artificial neural network model able to diagnose sepsis early based on the HeRO score along with a series of parameters strictly connected to the risk of neonatal sepsis. The proposed methodology shows promising results, outperforming the diagnostic accuracy of the only HeRO score and reducing the number of false positives, thus revealing itself to be a promising tool for supporting the clinicians in the daily clinical practice.