Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique

Cardiotocography (CTG) is a simultaneous recording of fetal heart rate (FHR) and uterine contractions (UC). It is one of the most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. Even few decades after the introduction of cardiotocography into clinical practice, the predictive capacity of the these methods remains controversial and still inaccurate. In this paper, we implement a model based CTG data classification system using a supervised artificial neural network(ANN) which can classify the CTG data based on its training data. According to the arrived results, the performance of the supervised machine learning based classification approach provided significant performance. We used Precision, Recall, F-Score and Rand Index as the metric to evaluate the performance. It was found that, the ANN based classifier was capable of identifying Normal, Suspicious and Pathologic condition, from the nature of CTG data with very good accuracy.

[1]  Mohd Alauddin Mohd Ali,et al.  A novel cardiotocography fetal heart rate baseline estimation algorithm , 2010 .

[2]  K. P. Kaliyamurthie,et al.  Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index , 2014 .

[3]  João Bernardes,et al.  Prediction of neonatal state by computer analysis of fetal heart rate tracings: the antepartum arm of the SisPorto multicentre validation study. , 2005, European journal of obstetrics, gynecology, and reproductive biology.

[4]  Timothy G. Overton Notes on Obstetrics and Gynaecology for the MRCOG, 5th edition , 2004 .

[5]  Eva Ocellkovš,et al.  CLASSIFICATION OF MULTISPECTRAL DATA , .

[6]  Michael Lloyd-Williams Discovering the Hidden Secrets in Your Data - the Data Mining Approach to Information , 1997, Inf. Res..

[7]  Eva Ocelíková,et al.  Multidimensional data classification , 2009 .

[8]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[9]  DengZhaohong,et al.  Enhanced soft subspace clustering integrating within-cluster and between-cluster information , 2010 .

[10]  Yunming Ye,et al.  A feature group weighting method for subspace clustering of high-dimensional data , 2012, Pattern Recognit..

[11]  Sri Krishna,et al.  Supervised Machine Learning Approaches for Medical Data Set Classification-A Review , 2022 .

[12]  Zhengxin Chen,et al.  A Descriptive Framework for the Field of Data Mining and Knowledge Discovery , 2008, Int. J. Inf. Technol. Decis. Mak..

[13]  Zhaohong Deng,et al.  Enhanced soft subspace clustering integrating within-cluster and between-cluster information , 2010, Pattern Recognit..