An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction

2017 ◽  
Vol 68 ◽  
pp. 163-172 ◽  
Author(s):  
Oluwarotimi Williams Samuel ◽  
Grace Mojisola Asogbon ◽  
Arun Kumar Sangaiah ◽  
Peng Fang ◽  
Guanglin Li
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Risk Prediction ◽  
Support System ◽  
Failure Risk

A Decision Support System for Heart Failure Risk Prediction Based on Weighted Naive Bayes

2021 ◽  
pp. 445-460
Author(s):  
Kehui Song ◽  
Shenglong Yu ◽  
Haiwei Zhang ◽  
Ying Zhang ◽  
Xiangrui Cai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Risk Prediction ◽  
Support System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Failure Risk

Clinical Decision Support System To Prevent Toxicity In Patients Treated With Digoxin

2010 ◽  
pp. 1-21
Author(s):  
Asunción Albert ◽  
Antonio J. Serrano ◽  
Emilio Soria ◽  
Nicolás Victor Jiménez
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Therapeutic Range ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Digitalis Toxicity

In this chapter, authors develop a system for prevention and detection of congestive heart failure and fibrillation. Due to its narrow therapeutic range more than 10% of the patients treated with DGX can suffer toxic effects, but it is estimated that half of the cases of digitalis toxicity could be prevented. Two multivariate models were developed to prevent digitalis toxicity.

scholarly journals Decision support system for the practical implementation of the Chronic Heart Failure guidelines: The MyHeart approach

2007 ◽  
Author(s):  
C. Bescos ◽  
M. Harris ◽  
R. Bover ◽  
R. Schmidt ◽  
J. Perez-Villacastin
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Chronic Heart Failure ◽  
Decision Support System ◽  
Support System ◽  
Practical Implementation

A Decision Support System for Aiding Heart Failure Management

2009 ◽  
Author(s):  
Sara Colantonio ◽  
Massimo Martinelli ◽  
Davide Moroni ◽  
Ovidio Salvetti ◽  
Franco Chiarugi ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Heart Failure Management

scholarly journals A Feature-Driven Decision Support System for Heart Failure Prediction Based on χ2 Statistical Model and Gaussian Naive Bayes

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Liaqat Ali ◽  
Shafqat Ullah Khan ◽  
Noorbakhsh Amiri Golilarz ◽  
Imrana Yakubu ◽  
Iqbal Qasim ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Decision Support System ◽  
Statistical Model ◽  
Decision Support Systems ◽  
Support System ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Support Systems ◽  
Naïve 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. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ2 statistical model is used to rank the commonly used 13 HF features. Based on the χ2 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 (χ2-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., χ2-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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