A Deep Learning–Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients

Xu, Da; Sheng, Jessica Qiuhua; Hu, Paul Jen-Hwa; Huang, Tsun‐Sheng; Hsu, Chih‐Chin

doi:10.1109/jbhi.2020.3033323

Cited by 20 publications

(8 citation statements)

References 51 publications

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“…The models must be expanded to include Patient Tracking with Long Range Wide-Area Network (LoRaWAN) [ 17 ], Deep Learning for Imputation of Missing Values in Patient Records [ 18 ], Multiple Objective Tabu Search (MTS) to solve Hospital Department Layout Tasks (HDLTs) [ 19 ], and Fluid Queueing Approximation (FQA) [ 20 ] for dynamic allocation of hospital and medical resources in response to large-scale requirements and multiple hospital scenarios. These models aim to solve hospital problems and simplify patient and resource management.…”

Section: Related Workmentioning

confidence: 99%

“…- May use mutable storage solutions. Fog Computing in Healthcare [ 18 ] - Introduces fault-tolerant data processing based on fog computing. - Enhances data processing and storage for healthcare applications.…”

Section: Related Workmentioning

confidence: 99%

“…These solutions not only do away with the need for additional bandwidth but also enable quick response in the case of a potentially life-threatening medical emergency on the premises. The paper [ 18 ] provides a fog computing-based fault-tolerant data processing layer as an illustration. Additionally [ 19 , 20 ], offers a framework for signal quality-aware data analysis of IoT device data.…”

Section: Introductionmentioning

confidence: 99%

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DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains

Reddy,

Debasis

2023

Heliyon

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains

Reddy,

Debasis

2023

Heliyon

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“…Li et al [61] mentioned that their method should be evaluated on other data types to verify its effectiveness for generalization. Xu et al [64] indicated that the limitation in dataset size and diversity could affect the imputation performance. In addition, missing ratios play a great role and require more attention because they can also affect the proposed method's performance [55].…”

Section: E Rq5 What Are the Limitations And Strength Points In Applyi...mentioning

confidence: 99%

Systematic Review of Using Machine Learning in Imputing Missing Values

et al. 2022

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Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learning has been utilized to replace conventional methods to address the problem of missing values more efficiently. By studying and analyzing recently proposed methods using machine learning approaches, vital adoptions in accuracy, performance, and time consumed can be highlighted. This study aimed to help data analysts and researchers address the limitations of machine learning imputation methods by conducting a systematic literature review to provide a comprehensive overview of using such methods to impute missing values. Novel proposed machine learning approaches used for data imputation are analyzed and summarized to assist researchers in selecting a proper machine learning method based on several factors and settings. The review was performed on research studies published between 2016 and 2021 on adopting machine learning to impute missing values, focusing on their strengths and limitations. A total of 684 research articles from various scientific databases were analyzed using search engines, and 94 of them were selected as primary studies. Finally, several recommendations were given to guide future researchers in applying machine learning to impute missing values.INDEX TERMS Systematic literature review, data imputation, data mining, missingness, data preprocessing, data quality.

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“…Heart health is influenced by a number of lifestyle choices, including cholesterol, pulse rate, and glucose management [10]. Heart disease can be caused by many things, but heredity, hypertension, cholesterol, age, gender, type of diet, calcium intake, the condition of the blood vessels, and how much they have been stretched all rank high on the list [11]. Death from heart disease has surpassed death from any other cause in many parts of the world.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Disorder Detection with a PSO-Optimized Bi-LSTM Recurrent Neural Network Model

Singirikonda,

Srivenkatesh

2023

IJRITCC

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The medical community is facing ever-increasing difficulties in identifying and treating cardiovascular diseases. The World Health Organization (WHO) reports that despite the availability of numerous high-priced medical remedies for persons with heart problems, CVDs continue to be the main cause of mortality globally, accounting for over 21 million deaths annually. When cardiovascular diseases are identified and treated early on, they cause far fewer deaths. Deep learning models have facilitated automated diagnostic methods for early detection of these diseases. Cardiovascular diseases often present insidious symptoms that are difficult to identify in a timely manner. Prompt diagnosis of individuals with CVD and related conditions, such as high blood pressure or high cholesterol, is crucial to initiate appropriate treatment. Recurrent neural networks (RNNs) with gated recurrent units (GRUs) have recently emerged as a more advanced variant, capable of surpassing Long Short-Term Memory (LSTM) models in several applications. When compared to LSTMs, GRUs have the advantages of faster calculation and less memory usage. When it comes to CVD prediction, the bio-inspired Particle Swarm Optimization (PSO) algorithm provides a straightforward method of getting the best possible outcomes with minimal effort. This stochastic optimization method requires neither the gradient nor any differentiated form of the objective function and emulates the behaviour and intelligence of swarms. PSO employs a swarm of agents, called particles, that navigate the search space to find the best prediction type.This study primarily focuses on predicting cardiovascular diseases using effective feature selection and classification methods. For CVD forecasting, we offer a GRU model built on recurrent neural networks and optimized with particle swarms (RNN-GRU-PSO). We find that the proposed model significantly outperforms the state-of-the-art models (98.2% accuracy in predicting cardiovascular diseases) in a head-to-head comparison.

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A Deep Learning–Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients

Cited by 20 publications

References 51 publications

DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains

DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains

Systematic Review of Using Machine Learning in Imputing Missing Values

Cardiovascular Disorder Detection with a PSO-Optimized Bi-LSTM Recurrent Neural Network Model

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