CEP4HFP: Complex Event Processing for Heart Failure Prediction

Mdhaffar, Afef; Rodriguez, Ismael Bouassida; Charfi, Khalil; Abid, Leïla; Freisleben, Bernd

doi:10.1109/tnb.2017.2769671

Cited by 36 publications

(15 citation statements)

References 13 publications

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“…However, the presence of additional features which made confusions in decision making which lead to increase the selection time and the reduction in accuracy. Mdhaffar et al (2017), designed a Complex Event Processing (CEP) technology with statistical approaches for analyzing the heart failure prediction in patients. The threshold customization approach was used to calculate the threshold values automatically and updated at runtime, which was the novelty of CEP.…”

Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Channabasavaraju¹,

Vinayakamurthy²

2020

Journal of Computer Science

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Data Mining Techniques are used in many areas like banking, healthcare, education because of extracting relevant information from the database. Many algorithms in data mining are used to predict the disease to reduce the patient treatment cost as well as increase the diagnosis accuracy. In this research work, the risk level of diabetic heart disease patient can be predicted using two datasets such as Pima Indians Diabetes dataset and heart disease dataset. Based on the World Health Organization (WHO), diabetes is the one of the biggest health concerns so mining the diabetes data is an ambiguous task. Diabetic patients may also have to suffer from other diseases like heart disease, eye complications, kidney disease, nerve damage, foot problems, skin complications and dental diseases. However, the existing techniques faced the difficulty to identify inconsistent and redundant features. In this research work, the Random Forest Feature Selection Technique (RFS) is developed to identify the significant features and eliminate irrelevant features which are used to improve the predicting accuracy of Cardio Vascular (CV) disease for diabetic patients. According to the output from the extensive experiments, this research categorizes that whether the diabetic patients having CV or not by using True Positive as diabetes and True Negative as no diabetes.

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Section: Literature Reviewmentioning

confidence: 99%

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

Channabasavaraju¹,

Vinayakamurthy²

2020

Journal of Computer Science

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“…The CEP aim at detecting complex events on the basis of an event space as a dataset. The CEP solutions has been applied successfully to predict heart failures [36], where simple events like symptoms are detected and hence an alert predicting the heart stroke (complex event) is enabled. Other applications include computer system failure prediction [6], Internet of things failure prediction [56] and bad traffic prediction [1].…”

Section: Event Oriented Approachesmentioning

confidence: 99%

A Taxonomy of Event Prediction Methods

Gmati

Chakhar²,

Chaari

et al. 2019

Lecture Notes in Computer Science

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Most of existing event prediction approaches consider event prediction problems within a specific application domain while event prediction is naturally a cross-disciplinary problem. This paper introduces a generic taxonomy of event prediction approaches. The proposed taxonomy, which oversteps the application domain, enables a better understanding of event prediction problems and allows conceiving and developing advanced and context-independent event prediction techniques.

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“…In the medical field, fault prediction based on structured data is already mature. Mdhaffar et al [ 4 ]. presented a novel health analysis approach for heart failure prediction.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering

Chang

Meng

et al. 2018

Entropy

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The purpose of this paper is to predict failures based on textual sequence data. The current failure prediction is mainly based on structured data. However, there are many unstructured data in aircraft maintenance. The failure mentioned here refers to failure types, such as transmitter failure and signal failure, which are classified by the clustering algorithm based on the failure text. For the failure text, this paper uses the natural language processing technology. Firstly, segmentation and the removal of stop words for Chinese failure text data is performed. The study applies the word2vec moving distance model to obtain the failure occurrence sequence for failure texts collected in a fixed period of time. According to the distance, a clustering algorithm is used to obtain a typical number of fault types. Secondly, the failure occurrence sequence is mined using sequence mining algorithms, such as-PrefixSpan. Finally, the above failure sequence is used to train the Bayesian failure network model. The final experimental results show that the Bayesian failure network has higher accuracy for failure prediction.

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CEP4HFP: Complex Event Processing for Heart Failure Prediction

Cited by 36 publications

References 13 publications

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

An Analysis of Heart Disease for Diabetic Patients Using Recursive Feature Elimination with Random Forest

A Taxonomy of Event Prediction Methods

A Bayesian Failure Prediction Network Based on Text Sequence Mining and Clustering

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