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Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise
Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can h...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217387/ https://www.ncbi.nlm.nih.gov/pubmed/37238178 http://dx.doi.org/10.3390/diagnostics13101692 |
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author | Kaliappan, Jayakumar Bagepalli, Apoorva Reddy Almal, Shubh Mishra, Rishabh Hu, Yuh-Chung Srinivasan, Kathiravan |
author_facet | Kaliappan, Jayakumar Bagepalli, Apoorva Reddy Almal, Shubh Mishra, Rishabh Hu, Yuh-Chung Srinivasan, Kathiravan |
author_sort | Kaliappan, Jayakumar |
collection | PubMed |
description | Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values. |
format | Online Article Text |
id | pubmed-10217387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102173872023-05-27 Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise Kaliappan, Jayakumar Bagepalli, Apoorva Reddy Almal, Shubh Mishra, Rishabh Hu, Yuh-Chung Srinivasan, Kathiravan Diagnostics (Basel) Article Intrauterine fetal demise in women during pregnancy is a major contributing factor in prenatal mortality and is a major global issue in developing and underdeveloped countries. When an unborn fetus passes away in the womb during the 20th week of pregnancy or later, early detection of the fetus can help reduce the chances of intrauterine fetal demise. Machine learning models such as Decision Trees, Random Forest, SVM Classifier, KNN, Gaussian Naïve Bayes, Adaboost, Gradient Boosting, Voting Classifier, and Neural Networks are trained to determine whether the fetal health is Normal, Suspect, or Pathological. This work uses 22 features related to fetal heart rate obtained from the Cardiotocogram (CTG) clinical procedure for 2126 patients. Our paper focuses on applying various cross-validation techniques, namely, K-Fold, Hold-Out, Leave-One-Out, Leave-P-Out, Monte Carlo, Stratified K-fold, and Repeated K-fold, on the above ML algorithms to enhance them and determine the best performing algorithm. We conducted exploratory data analysis to obtain detailed inferences on the features. Gradient Boosting and Voting Classifier achieved 99% accuracy after applying cross-validation techniques. The dataset used has the dimension of 2126 × 22, and the label is multiclass classified as Normal, Suspect, and Pathological condition. Apart from incorporating cross-validation strategies on several machine learning algorithms, the research paper focuses on Blackbox evaluation, which is an Interpretable Machine Learning Technique used to understand the underlying working mechanism of each model and the means by which it picks features to train and predict values. MDPI 2023-05-10 /pmc/articles/PMC10217387/ /pubmed/37238178 http://dx.doi.org/10.3390/diagnostics13101692 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kaliappan, Jayakumar Bagepalli, Apoorva Reddy Almal, Shubh Mishra, Rishabh Hu, Yuh-Chung Srinivasan, Kathiravan Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise |
title | Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise |
title_full | Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise |
title_fullStr | Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise |
title_full_unstemmed | Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise |
title_short | Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise |
title_sort | impact of cross-validation on machine learning models for early detection of intrauterine fetal demise |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217387/ https://www.ncbi.nlm.nih.gov/pubmed/37238178 http://dx.doi.org/10.3390/diagnostics13101692 |
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