Diagnosing Abnormality of Foetus Using Machine Learning Algorithms

This is the authors' abstract. We don't add key points for this paper.

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.