Predicting the Type of Nanostructure Using Data Mining Techniques and Multinomial Logistic Regression

Cardiotocography (CTG) is a biophysical method for assessing fetal condition that primarily relies on the recording and automated analysis of fetal heart activity. The quantitative description of the CTG signals is provided by computerized fetal monitoring systems. Even though effective conclusion generation methods for decision process support are still required to find out the fetal risk such as premature embryo, this proposed method and outcome data can confirm the assessment of the fetal state after birth. Low birth weight is quite possibly the main attribute that significantly depicts an unusual fetal result. These expectations are assessed in a constant experimental decision support system, providing valuable information that can be used to obtain additional information about the fetal state using machine learning techniques. The advancements in modern obstetric practice enabled the use of numerous reliable and robust machine learning approaches in classifying fetal heart rate signals. The Naïve Bayes (NB) classifier, support vector machine (SVM), decision trees (DT), and random forest (RF) are used in the proposed method. To assess these outcomes in the proposed method, some of the metrics such as precision, accuracy, F1 score, recall, sensitivity, logarithmic loss and mean absolute error have been taken. The above mentioned metrics will be helpful to predict the fetal risk.

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Predicting the Type of Nanostructure Using Data Mining Techniques and Multinomial Logistic Regression

Cited by 2 publications

References 16 publications

Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set

Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set

Machine Learning Techniques for Identifying Fetal Risk During Pregnancy

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