• S.Santhiya
• G.Kowreesh
• G.V.Chitraleka
• P.Boobalaragavan

Children are the future of the world. So, this work wants to be sure that they are born without any complications and disabilities. But here there are many new born infants who suffered from complicated disorders like fetal distress, which is the complication which arises during delivery. This condition is basically caused because of insufficient oxygen supply and lack of other vital factors inside their mother’s womb. This distress will lead to some dangerous disorders like neurological malfunctions and sometimes it can also lead to fatal. Cardiotocography is an effective, efficient and widely used method to analyze the fetal heart beat rate (FHR) and uterine contraction of the mother and so on. This cardiotocography plays an important role in analyzing various fetal distress conditions and their possibilities. So, let us built a machine learning model which analyses the results and observations made during the cardiotocography and predicts the possibilities of the fetal distress to be occurred.

Cardiotocography, Fetal distress, Random forest, Decision Trees, Fetal Heart Rate

"FETAL DISTRESS CLASSIFICATION USING CARDIOTOCOGRAPHY ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 8, page no.39-46, August-2020, Available :http://www.jetir.org/papers/JETIREF06007.pdf

"FETAL DISTRESS CLASSIFICATION USING CARDIOTOCOGRAPHY ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 8, page no. pp39-46, August-2020, Available at : http://www.jetir.org/papers/JETIREF06007.pdf