Analysis of multi-diseases using big data for improvement in healthcare

Adil, Asif; Kar, Hushmat Amin; Jangir, Rajendra; Sofi, Shabir Ahmad

doi:10.1109/upcon.2015.7456696

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Hadoop, which supports distributed storage and computation of immense unstructured datasets across clusters of computers, is an Apache open source framework written in Java. Hadoop is also calculated to scale horizontally from a single server to thousands of machines, each offering local computation and storage (Adil et al, 2015). The MapReduce framework is effective in enabling dynamical big datasets, however attribute reduction algorithms may lead to the high computation complexity.…”

Section: Methodsmentioning

confidence: 99%

“…A variety of sources can be utilized to collect large-scale data. For Example, some types of healthcare data that is available for Big Data Analytics (BDA) consists of hospital data, clinical data, genomic data, streamed data, global health survey data, clinical reference data and health publications, WHO (World Health Organization) repositories, web and social networking data (Adil et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Healthcare Driven by Big Data Analytics

Alexander

Wang²

2018

American Journal of Engineering and Applied Sciences

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Text messages and social network posts are often included in big data and are frequently invaluable sources of health data. When machine learning or data mining is used, it is important to perform an automatic process for integrating all available and related health data. Artificial Neural Network (ANN) is one of the machine learning methods flexible in using algorithms to detect complicated nonlinear relationships within huge datasets. Pharmacokinetics uses genetics to individualize drug therapy. Because genetic and pharmacological analysis often require large scale computation, Big Data analytics has shown potential in this area. Personal data from various sensors can often be used for making health and treatment recommendations, taking appropriate action for a patient's lifestyle choices and early diagnosis vital to advancing quality of care. Big data techniques such as hadoop and spark have been used in various areas of healthcare and big data analytics has been employed in great datasets to expose hidden patterns or correlations for effective decision-making. Challenges of big data in healthcare, concerned with gathering material from multifaceted heterogonous patient sources still exist, although hadoop has been employed as the processing unit for heterogeneous data gathered from various body sensors. The most critical necessity in a healthcare big data system is the data security; however, users are continuously applying big data-driven strategies to solve the various problems and challenges.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Healthcare Driven by Big Data Analytics

Alexander

Wang²

2018

American Journal of Engineering and Applied Sciences

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“…The data-intensive scientific discoveries rely on three paradigms-theory, experimentation, and simulation modeling (Tolle et al, 2011). As big data is described with three characteristics (volume, velocity, and variety) (Stephens et al, 2015;Adil et al, 2016), data generated by SC-RNA-seq are tantamount to these three quantitative characteristics (Ivanov et al, 2013). With the introduction of new methods in microfluidics (Zare and Kim, 2010), combinatorial indexing procedures (Fan et al, 2015), and rapid drop in the sequencing cost, SC assay profiling has widely become a routine practice among biologists for analyzing millions of cells in hours, paving the way for the accumulation of a large amount of data.…”

Section: Big Data Pertaining To Single-cell Rna Sequencingmentioning

confidence: 99%

Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

et al. 2021

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Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

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“…The usage of big data, will give different effectiveoutcomes across large population. [3] Surveys of more than a decade can be processed and analyzed to check the diseases that come at a particular period .…”

Section: Introductionmentioning

confidence: 99%

Analysis of Multi-Disease & Prediction of Suitable Drug for Healthcare Application using Bigdata

Patidar¹

2017

IJRASET

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The Healthcare technologies have grown immensely in various domains. These technologies have also made the health care data huge making it difficult to process. Several precautions should be taken using pharmaceutical drugs, for both healthcare professionals, who prescribe and administer drugs, and for drug consumers. To achieve this goal, we integrate the massive data using Hadoop and perform a wide range of medical and healthcare functions to infer knowledge that assist in diagnosis of disease and provide the best drug. User can post the query through system. We also arrange appointment to the Best Doctor for the consultation based on user feedbacks.

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Analysis of multi-diseases using big data for improvement in healthcare

Cited by 9 publications

References 8 publications

Healthcare Driven by Big Data Analytics

Healthcare Driven by Big Data Analytics

Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

Analysis of Multi-Disease & Prediction of Suitable Drug for Healthcare Application using Bigdata

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