Data Analysis and Optimal Prediction Model Search based on CTG Data

Abstract

This paper presents a data analysis and optimal prediction model search of fetal health based on cardiotocography (CTG) data. The objective of this study is to develop an accurate and efficient method to predict fetal health outcomes using CTG data. This paper first analyzes the dataset to identify potential predictors of fetal health and investigate their relationship with fetal distress. The result found a strong relationship between some of the variables in the dataset and fetal health. This paper then uses machine learning algorithms to build and compare several prediction models, including elastic net regression and lasso model. The result of this paper shows that a random forest model performs best in terms of AUC. The model can accurately predict fetal health outcomes with an AUC of 0.925. The finding of this paper suggests that CTG data analysis combined with machine learning algorithms can provide a useful tool for prenatal monitoring and management.