Retraction Note: LSTM Model for Prediction of Heart Failure in Big Data

Maragatham, G.; Devi, S. Prasanna

doi:10.1007/s10916-022-01830-2

Cited by 8 publications

(20 citation statements)

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Section: Literature Reviewmentioning

confidence: 99%

“…Several heart diagnosis studies in the state of the art [3,[6][7][8][9][10][11][12][13][14] have been done extraordinary works that contributed by providing different prediction approaches. These studies could be categorized based on the targeted prediction such as Heart Failure (HF) prediction [7], mortality or hospitalization prediction of the HF patient [3,6], and EHD prediction [8][9][10][11][12][13][14]. In addition, they also could be categorized in terms of the investigated learning technique, whether it is supervised learning [3,8,11,12], ensemble learning [6,12], deep learning [7,9] or even hybrid learning [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…These studies could be categorized based on the targeted prediction such as Heart Failure (HF) prediction [7], mortality or hospitalization prediction of the HF patient [3,6], and EHD prediction [8][9][10][11][12][13][14]. In addition, they also could be categorized in terms of the investigated learning technique, whether it is supervised learning [3,8,11,12], ensemble learning [6,12], deep learning [7,9] or even hybrid learning [10]. Table I summaries the recent related works [3,6,7,[10][11][12] in terms of their experimented datasets, pre-processing techniques, learning methodologies, performance evaluation and drawbacks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While in 1998, the second stage started when Wilson et al [5] established a new direction concerning heart diseases estimation by utilizing risk factor classes with the aid of logistic approaches and regression calculations. Nowadays, several researchers have developed various machine learning algorithms [3,[6][7][8][9][10] to predict the EHD on the publicly available datasets which are the focus of this work.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative Analysis of Machine Learning Algorithms and Data Mining Techniques for Predicting the Existence of Heart Disease

Alotaibi¹,

Alzahrani²

2022

IJACSA

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Heart diseases are considered one of the leading causes of death globally over the world. They are difficult to be predicted by a specialist physician as it is not an easy task which requires greater knowledge and expertise for prediction. With the variety of machine learning and deep learning algorithms, there exist many recent studies in the state of the art that have been done remarkable and practical works for predicting the presence of heart diseases. However, some of these works were affected by various drawbacks. Hence, this work aims to compare and analyze different classifiers, pre-processing, and dimensionality reduction techniques (feature selection and feature extraction) and study their effect on the prediction of heart diseases existence. Therefore, based on the resulting performance of several conducted experiments on the well-known Cleveland heart disease dataset, the findings of this study are: 1) the most significant subset of features to predict the existence of heart diseases are PES, EIA, CPT, MHR, THA, VCA, and OPK, 2) Naïve Bayes classifier gave the best performance prediction, and 3) Chi-squared feature selection was the data mining technique that reduced the number of features while maintained the same improved performance for predicting the presence of heart disease.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Analysis of Machine Learning Algorithms and Data Mining Techniques for Predicting the Existence of Heart Disease

Alotaibi¹,

Alzahrani²

2022

IJACSA

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“…Recurrent neural networks such as LSTM [16], GRU [7] and GRU-D [5] have been used widely for EHR patient journey data modeling as representative deep learning models. Examples of successful applications include heart failure prediction [10,29], and comorbidity prediction and patient similarity analysis [33]. The performance of such sequential models is limited, especially for understanding the timing of visits, because the RNN architecture only contains recurrence (i.e., the order of sequences/visits).…”

Section: Related Workmentioning

confidence: 99%

Modeling long-term dependencies and short-term correlations in patient journey data with temporal attention networks for health prediction

Liu

Zhang

Yepes

et al. 2022

Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Building models for health prediction based on Electronic Health Records (EHR) has become an active research area. EHR patient journey data consists of patient time-ordered clinical events/visits from patients. Most existing studies focus on modeling long-term dependencies between visits, without explicitly taking short-term correlations between consecutive visits into account, where irregular time intervals, incorporated as auxiliary information, are fed into health prediction models to capture latent progressive patterns of patient journeys. We present a novel deep neural network with four modules to take into account the contributions of various variables for health prediction: i) the Stacked Attention module strengthens the deep semantics in clinical events within each patient journey and generates visit embeddings, ii) the Short-Term Temporal Attention module models short-term correlations between consecutive visit embeddings while capturing the impact of time intervals within those visit embeddings, iii) the Long-Term Temporal Attention module models long-term dependencies between visit embeddings while capturing the impact of time intervals within those visit embeddings, iv) and finally, the Coupled Attention module adaptively aggregates the outputs of Short-Term Temporal Attention and Long-Term Temporal Attention modules to make health predictions. Experimental results on MIMIC-III demonstrate superior predictive accuracy of our model compared to existing state-of-the-art methods, as well as the interpretability and robustness of this approach. Furthermore, we found that modeling short-term correlations contributes to local priors generation, leading to improved predictive modeling of patient journeys.

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Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

Jia

Zhu

et al. 2022

IMDS

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PurposeAbundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.Design/methodology/approachAs the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.FindingsThe empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.Practical implicationsWith these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.Originality/valueTo address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

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Retraction Note: LSTM Model for Prediction of Heart Failure in Big Data

Cited by 8 publications

References 0 publications

Comparative Analysis of Machine Learning Algorithms and Data Mining Techniques for Predicting the Existence of Heart Disease

Comparative Analysis of Machine Learning Algorithms and Data Mining Techniques for Predicting the Existence of Heart Disease

Modeling long-term dependencies and short-term correlations in patient journey data with temporal attention networks for health prediction

Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

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