DIAGNOSING ABNORMALITY OF FOETUS USING MACHINE LEARNING ALGORITHMS - Ragav, Abhijith; P.Surya; S.G, Shivabhinav; P.Akilandeswari (International Journal of Psychosocial Rehabilitation, 2020)
The primary objective of the paper is to utilise machine learning algorithms for diag- nosing sufferance caused to the foetus by parameters such as Fetal Heart Rate (FHR) recordings (section). The significance of this work is to accurately predict the foetus condition at a much earlier stage since it is very important to analyse the issue at the right time to avoid complications. Concluding results from Cardiotocography (CTG), a test which is used widely for estimating fetal distress poses a major challenge. This work benefits the medical community, by detecting the fetal distress conditions using features derived from CTG results at a preliminary stage of 30-35 gestational weeks. The acute state can be classified by a few indications such as decrease in oxygen content due to reduction of haemoglobin count of fetal unit and is usually a complication of labour. The paper deals with the prior prediction of foetal conditions so as to provide early and effective treatment.