DIAGNOSING ABNORMALITY OF FOETUS USING MACHINE LEARNING ALGORITHMS

1Abhijith Ragav, P.Surya, Shivabhinav.S.G, P.Akilandeswari

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Abstract:

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.

Keywords:

Fetal Heart Rate, Cardiotocography, K Nearest neighbours, Support Vector, Machine, Radial Basis Function, Extreme Gradient Boosting.

Paper Details
Month2
Year2020
Volume24
IssueIssue 8
Pages12897-12905