A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes

Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems....

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Autores principales: Ali, Liaqat, Khan, Shafqat Ullah, Golilarz, Noorbakhsh Amiri, Yakubu, Imrana, Qasim, Iqbal, Noor, Adeeb, Nour, Redhwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925936/
https://www.ncbi.nlm.nih.gov/pubmed/31885684
http://dx.doi.org/10.1155/2019/6314328
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author Ali, Liaqat
Khan, Shafqat Ullah
Golilarz, Noorbakhsh Amiri
Yakubu, Imrana
Qasim, Iqbal
Noor, Adeeb
Nour, Redhwan
author_facet Ali, Liaqat
Khan, Shafqat Ullah
Golilarz, Noorbakhsh Amiri
Yakubu, Imrana
Qasim, Iqbal
Noor, Adeeb
Nour, Redhwan
author_sort Ali, Liaqat
collection PubMed
description Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ(2) statistical model is used to rank the commonly used 13 HF features. Based on the χ(2) test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ(2)-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ(2)-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%.
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spelling pubmed-69259362019-12-29 A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes Ali, Liaqat Khan, Shafqat Ullah Golilarz, Noorbakhsh Amiri Yakubu, Imrana Qasim, Iqbal Noor, Adeeb Nour, Redhwan Comput Math Methods Med Research Article Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ(2) statistical model is used to rank the commonly used 13 HF features. Based on the χ(2) test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ(2)-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ(2)-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%. Hindawi 2019-11-20 /pmc/articles/PMC6925936/ /pubmed/31885684 http://dx.doi.org/10.1155/2019/6314328 Text en Copyright © 2019 Liaqat Ali et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Ali, Liaqat
Khan, Shafqat Ullah
Golilarz, Noorbakhsh Amiri
Yakubu, Imrana
Qasim, Iqbal
Noor, Adeeb
Nour, Redhwan
A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes
title A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes
title_full A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes
title_fullStr A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes
title_full_unstemmed A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes
title_short A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ(2) Statistical Model and Gaussian Naive Bayes
title_sort feature-driven decision support system for heart failure prediction based on χ(2) statistical model and gaussian naive bayes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925936/
https://www.ncbi.nlm.nih.gov/pubmed/31885684
http://dx.doi.org/10.1155/2019/6314328
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