• M.G. VINODH ARRUN
• A. ARSHIYA BANU
• M. DEEPA
• S. DEEPA
• S. GEETHA

Early-onset sepsis in babies refers to an infection that occurs in newborns within the first few days of life, typically within the first 72 hours after birth. This type of sepsis is often caused by bacteria that the baby acquires from the mother during delivery, or from exposure to other infections in the neonatal intensive care unit. Early-onset sepsis can be life-threatening and requires immediate medical attention. This type of sepsis can be caused by a variety of bacteria and other pathogens, and can occur in babies who are otherwise healthy as well as in those who are already sick or premature. Both early-onset and late-onset sepsis are serious conditions that require early detection and treatment to prevent complications and improve outcomes. The use of sensors and machine learning algorithms to predict sepsis can help identify early warning signs of the disease, allowing for earlier intervention and better outcomes for affected infants. Early detection and timely treatment are critical to improving outcomes for these vulnerable patients. Machine learning (ML) algorithms have shown promise in predicting the onset of sepsis in new-borns by analysing data from various sources, including vital signs monitors, electronic health records, and laboratory reports. Early prediction of sepsis in babies is crucial as sepsis can progress rapidly and lead to serious complications if left untreated. The use of sensors such as heart rate, ECG, and force sensors can help detect early signs of sepsis in babies.Collecting the data from sensor and using ML for the prediction of sepsis in new-borns include data collection, feature selection, algorithm development, real-time monitoring, early detection, treatment personalization, and continuous improvement.The use of ML in predicting sepsis in new-borns represents a promising approach to improving sepsis diagnosis and treatment, and has the potential to significantly reduce mortality rates associated with sepsis in this vulnerable patient population.

Early-onset sepsis (EOS), Late-onset sepsis (LOS) and Bacterial infections.

"NEONATAL DEATH AVOIDENCE DEVICE AID MACHINE LEARNING BASED LATE ON SEPSIS PREDICTION SYSTEM WITH IOT MONITORING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.l237-l244, April-2023, Available :http://www.jetir.org/papers/JETIR2304B37.pdf

"NEONATAL DEATH AVOIDENCE DEVICE AID MACHINE LEARNING BASED LATE ON SEPSIS PREDICTION SYSTEM WITH IOT MONITORING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppl237-l244, April-2023, Available at : http://www.jetir.org/papers/JETIR2304B37.pdf