Fetal health classification from cardiotocographic data using machine learning

2021 ◽  
Author(s):  
Abolfazl Mehbodniya ◽  
Arokia Jesu Prabhu Lazar ◽  
Julian Webber ◽  
Dilip Kumar Sharma ◽  
Santhosh Jayagopalan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fetal Health

scholarly journals Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 30-40
Author(s):  
Pankaj Bhowmik ◽  
◽  
Pulak Chandra Bhowmik ◽  
U. A. Md. Ehsan Ali ◽  
Md. Sohrawordi
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Health Risks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Model Assessment ◽  
Decision Tree Classifier ◽  
Chi Square ◽  
Fetal Health ◽  
Tree Classifier

A sizeable number of women face difficulties during pregnancy, which eventually can lead the fetus towards serious health problems. However, early detection of these risks can save both the invaluable life of infants and mothers. Cardiotocography (CTG) data provides sophisticated information by monitoring the heart rate signal of the fetus, is used to predict the potential risks of fetal wellbeing and for making clinical conclusions. This paper proposed to analyze the antepartum CTG data (available on UCI Machine Learning Repository) and develop an efficient tree-based ensemble learning (EL) classifier model to predict fetal health status. In this study, EL considers the Stacking approach, and a concise overview of this approach is discussed and developed accordingly. The study also endeavors to apply distinct machine learning algorithmic techniques on the CTG dataset and determine their performances. The Stacking EL technique, in this paper, involves four tree-based machine learning algorithms, namely, Random Forest classifier, Decision Tree classifier, Extra Trees classifier, and Deep Forest classifier as base learners. The CTG dataset contains 21 features, but only 10 most important features are selected from the dataset with the Chi-square method for this experiment, and then the features are normalized with Min-Max scaling. Following that, Grid Search is applied for tuning the hyperparameters of the base algorithms. Subsequently, 10-folds cross validation is performed to select the meta learner of the EL classifier model. However, a comparative model assessment is made between the individual base learning algorithms and the EL classifier model; and the finding depicts EL classifiers’ superiority in fetal health risks prediction with securing the accuracy of about 96.05%. Eventually, this study concludes that the Stacking EL approach can be a substantial paradigm in machine learning studies to improve models’ accuracy and reduce the error rate.

scholarly journals Comparison of machine learning algorithms to classify fetal health using cardiotocogram data

2022 ◽  
Vol 197 ◽  
pp. 162-171
Author(s):  
Nabillah Rahmayanti ◽  
Humaira Pradani ◽  
Muhammad Pahlawan ◽  
Retno Vinarti
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Fetal Health

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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