Clinical Big Data Predictive Analytics Transforming Healthcare: - An Integrated Framework for Promise Towards Value Based Healthcare

Naqishbandi, Tawseef Ahmad; Ayyanathan, N.

doi:10.1007/978-3-030-24318-0_64

Cited by 10 publications

(4 citation statements)

References 49 publications

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“… No Citation Technique Description 1 56 Network foresight approach To provide an easy and timely data-access interface this research work used networked foresight approach for the prediction of accurate medicine using current and past clinical historical information. This approach provides an easier interface for the patients and doctors to communicate and exchange information regarding their health status 2 8 – 11 , 19 , 57 , 63 , 73 , 78 , 82 , 86 , 92 , 95 , 97 , 111 – 113 , 116 , 120 , 123 – 126 , 128 , 129 , 131 – 133 , 135 , 137 , 139 – 141 , 144 – 147 , 149 – 154 , 156 , 157 Big data analytics for timely data-access purposes These survey papers presents applications of big data analytics in term of facilities like presenting an easier and timely-access mechanism for both patients and practitioners 3 70 , 197 Pig Latin Script programming tool Pig Latin Script is applied to analyse the healthcare datasets against different queries, to enhance health facilities in ...…”

Section: Resultsmentioning

confidence: 99%

“…To provide an easy and timely data-access interface this research work used networked foresight approach for the prediction of accurate medicine using current and past clinical historical information. This approach provides an easier interface for the patients and doctors to communicate and exchange information regarding their health status 2 [8][9][10][11]19,57,63,73,78,82,86,92,95,97,[111][112][113]116,120,[123][124][125][126]128,129,[131][132][133]135,137,[139][140][141][144][145][146][147][149][150][151][152][153][154]156,157 Big data analytics for timely data-access purposes…”

Section: Network Foresight Approachmentioning

confidence: 99%

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Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Khan

Nazir

2022

Sci Rep

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Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

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Section: Resultsmentioning

confidence: 99%

Section: Network Foresight Approachmentioning

confidence: 99%

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Khan

Nazir

2022

Sci Rep

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“…References [24,25] deliberated the basics of predictive analytics in healthcare. In our system, RBF acts as a non-linear kernel for SVM with respect to study.…”

Section: Introductionmentioning

confidence: 99%

Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification

Sujitha¹,

Paramasivan²

2022

Computers, Materials &Amp; Continua

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With the modernization of machine learning techniques in healthcare, different innovations including support vector machine (SVM) have predominantly played a major role in classifying lung cancer, predicting coronavirus disease 2019, and other diseases. In particular, our algorithm focuses on integrated datasets as compared with other existing works. In this study, parallel-based SVM (P-SVM) and multiclass-based multiple submodels (MMSM-SVM) were used to analyze the optimal classification of lung diseases. This analysis aimed to find the optimal classification of lung diseases with id and stages, such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses, respectively. For nonlinear classification, kernel clustering-based SVM embedded with multiple submodels was developed. Both algorithms were developed using Apache spark environment, and data for the analysis were retrieved from microscope lab, UCI, Kaggle, and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 GB. Performance measures were conducted using a 5 GB dataset with five nodes. Dataset size was finally increased, and task analysis and CPU utilization were measured.

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“…The algorithms are implemented and root mean square has been calculated for measuring the demand needs in power generation. The application is used for determining the requirement of power distribution in various energy distributions [24], [25]. The ANN model is used for predicting the power consumption for quick estimating the energy needs in both demand and requirement of energy variables.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of demand forecasting in energy consumption based on ensemble model

Dhanalakshmi

Ayyanathan

2022

Bulletin EEI

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Over the previous decade, energy usage has increased exponentially all over the world. The machine learning algorithms are used to classify the demand and requirement of off and evening peak load of southern regional load dispatch centre (SRLDC) data. In this paper, data are classified based on demand and requirement of both evening and off peak of day wise southern regional grid of Andhra Pradesh, Karnataka, Kerala, Tamilnadu, and Pondicherry of different states are proposed. The machine learning algorithms like k-nearest neighbors (KNN), random forest, and logistic regression have been adopted to classify the model. To improve this model efficiency, an ensemble learning method is used to increase the accuracy. The performance measure of state-wise outcome is determined by classifying its demand and requirement needs over its state energy consumption and with different classification algorithms and it is improved by using a combined method of ensemble model with accuracy of 86%.

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Clinical Big Data Predictive Analytics Transforming Healthcare: - An Integrated Framework for Promise Towards Value Based Healthcare

Cited by 10 publications

References 49 publications

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification

Performance analysis of demand forecasting in energy consumption based on ensemble model

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