Effective Analytics on Healthcare Big Data Using Ensemble Learning

Mung, Pau Suan; Phyu, Sabai

doi:10.1109/icca49400.2020.9022853

Cited by 18 publications

(6 citation statements)

References 7 publications

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“…The authors in [2] describes big data in healthcare is a collection of records i.e., patient, doctor, hospital, medical treatment. It's difficult to manage heterogeneous data and obviously it's a big challenge to deal with the reliability of data and the normal data set frameworks are sufficiently not to maintain information.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The research gap in this paper is that the scalability of proposed framework is used in this article that can't be compared with other platforms to enhance performance as only a single IMP platform is used which can't give the desired results i.e., IMP can be compared with other platforms to increase the performance [1]. In this article, [2] soft voting method is used. It includes gather the data, feature selection and data cleaning and developing the model using ensemble learning method which has more accuracy than the individual classifiers.…”

Section: Explanation Of the Tablementioning

confidence: 99%

“…The articles that we examined the accompanying stages are utilized in huge information investigation viz Intelligent Medical Platform, content-based sifting, EHR (Electronic Health Records), methodical writing convention, SNOMED CT all CP information, metaphysics based demonstrating and HL 7 correspondence, WSRMax, PF and Hadoop [1], [2], [11], [12], [3]- [10]. As per our perspective after steps have the accompanying downsides which are as per the following; intelligent clinical stage can charge a big amount of time and money to construct, modify, and fix.…”

Section: Critical Analysis and Future Workmentioning

confidence: 99%

See 2 more Smart Citations

The Role of Big Data Analytics in Healthcare

Khan¹,

Younas²,

Khan³

et al. 2021

IJSCE

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Recently a rapid increase has been seen in the technology and health care became an unavoidable sector because an enormous amount of data is collected day to day. The Big data analytics has many uses and it is associated to big data. It gives an important judgment in the area of health care. This paper aims to present a review of big data in health care that how big data can aid the healthcare. In this paper multiple research paper has been reviewed and the limitations and research gaps of these papers has been discussed accordingly.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Explanation Of the Tablementioning

confidence: 99%

Section: Critical Analysis and Future Workmentioning

confidence: 99%

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The Role of Big Data Analytics in Healthcare

Khan¹,

Younas²,

Khan³

et al. 2021

IJSCE

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“…The application of neural network ensembles [35] can improve the effectiveness of the system in comparison with a single network realization. The collective voting for the best answer has already proven to be a valid approach to the problems of Hyperspectral Image Classification [36], co-reference resolution [37], Computer Vision and NLP [38], and healthcare [39]. Consequently, it is worth deploying this idea to address the mine detection problem.…”

Section: Introductionmentioning

confidence: 99%

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

et al. 2022

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Artificial Neural Network (ANN) approaches are applied to detect and determine the object class using a special set of the UltraWideBand (UWB) pulse Ground Penetrating Radar (GPR) sounding results. It used the results of GPR sounding with the antenna system, consisting of one radiator and four receiving antennas located around the transmitting antenna. The presence of four receiving antennas and, accordingly, the signals received from four spatially separated positions of the antennas provide a collection of signals received after reflection from an object at different angles and, due to this, to determine the location of the object in a coordinate system, connected to the antenna. We considered the sums and differences of signals received by two of the four antennas in six possible combinations: (1 and 2, 1 and 3, 2 and 3, 1 and 4, etc.). These combinations were then stacked sequentially one by one into one long signal. Synthetic signals constructed in such a way contain many more notable differences and specific information about the class to which the object belongs as well as the location of the searched object compared to the signals obtained by an antenna system with just one radiating and one receiving antenna. It therefore increases the accuracy in determining the object’s coordinates and its classification. The pulse radiation, propagation, and scattering are numerically simulated by the finite difference time domain (FDTD) method. Results from the experiment on mine detection are used to examine ANN too. The set of signals from different objects having different distances from the GPR was used as a training and testing dataset for ANN. The training aims to recognize and classify the detected object as a landmine or other object and to determine its location. The influence of Gaussian noise added to the signals on noise immunity of ANN was investigated. The recognition results obtained by using an ANN ensemble are presented. The ensemble consists of fully connected and recurrent neural networks, gated recurrent units, and a long-short term memory network. The results of the recognition by all ANNs are processed by a meta network to provide a better quality of underground object classification.

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“…11 In addition, myriad studies have reported the potential of ensemble learning algorithms in predictive tasks. 12,13 In the current study, we assessed the performance metrics of the three powerful ensemble learning algorithms. Due to skewed or imbalanced distribution of the outcome of interest, we further assessed whether the synthetic minority oversampling technique (SMOTE), Borderline-SMOTE and random undersampling (RUS) techniques would impact the learning process of the models.…”

Section: Introductionmentioning

confidence: 99%

Combining Resampling Strategies and Ensemble Machine Learning Methods to Enhance Prediction of Neonates with a Low Apgar Score After Induction of Labor in Northern Tanzania

Tarimo¹,

Bhuyan²,

Li³

et al. 2021

RMHP

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The goal of this study was to establish the most efficient boosting method in predicting neonatal low Apgar scores following labor induction intervention and to assess whether resampling strategies would improve the predictive performance of the selected boosting algorithms. Methods: A total of 7716 singleton births delivered from 2000 to 2015 were analyzed. Cesarean deliveries following labor induction, deliveries with abnormal presentation, and deliveries with missing Apgar score or delivery mode information were excluded. We examined the effect of resampling approaches or data preprocessing on predicting low Apgar scores, specifically the synthetic minority oversampling technique (SMOTE), borderline-SMOTE, and the random undersampling (RUS) technique. Sensitivity, specificity, precision, area under receiver operating curve (AUROC), F-score, positive predicted values (PPV), negative predicted values (NPV) and accuracy of the three (3) boosting-based ensemble methods were used to evaluate their discriminative ability. The ensemble learning models tested include adoptive boosting (AdaBoost), gradient boosting (GB) and extreme gradient boosting method (XGBoost). Results: The prevalence of low (<7) Apgar scores was 9.5% (n = 733). The prediction models performed nearly similar in their baseline mode. Following the application of resampling techniques, borderline-SMOTE significantly improved the predictive performance of all the boosting-based ensemble methods under observation in terms of sensitivity, F1-score, AUROC and PPV. Conclusion: Policymakers, healthcare informaticians and neonatologists should consider implementing data preprocessing strategies when predicting a neonatal outcome with imbalanced data to enhance efficiency. The process may be more effective when borderline-SMOTE technique is deployed on the selected ensemble classifiers. However, future research may focus on testing additional resampling techniques, performing feature engineering, variable selection and optimizing further the ensemble learning hyperparameters.

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Effective Analytics on Healthcare Big Data Using Ensemble Learning

Cited by 18 publications

References 7 publications

The Role of Big Data Analytics in Healthcare

The Role of Big Data Analytics in Healthcare

Implementation of an Artificial Intelligence Approach to GPR Systems for Landmine Detection

Combining Resampling Strategies and Ensemble Machine Learning Methods to Enhance Prediction of Neonates with a Low Apgar Score After Induction of Labor in Northern Tanzania

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