Big Data Analytics for Medical Applications

Das, N. R.; Das, Leena; Rautaray, Siddharth Swarup; Pandey, Manjusha

doi:10.5815/ijmecs.2018.02.04

Cited by 33 publications

(15 citation statements)

References 29 publications

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“…This analysis is important for business purpose and society to get better results with smart decision making for solving inaccurate data analysis as well as to reduce risk and cost. The main concerns that need to be considered while implementing of BDA for the business are interoperability, reusability, manageability, security and maturity [34]. BDA could lead to innovation in education especially in HE because it can help this institution to transform the existing process such as administration, academic work, teaching and learning process to be more effective.…”

Section: Big Data Analyticsmentioning

confidence: 99%

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Tarmizi¹,

Mutalib²,

Hamid³

et al. 2019

IJMECS

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Student attrition among undergraduate students is among the most concerned issues in higher educational institutions in Malaysia and abroad. This problem arises when these students unable to complete their studies within the stipulated period when there are majoring in the Science, Technology, Engineering, and Mathematics (STEM) fields. Research findings highlight numerous factors contribute to the student attrition. These findings also suggest that the factors differ from one case to another case. Effects of student attrition not only for the student itself but also to the institutions and community. It is challenging to classify the factors based on general assumptions. Moreover, increasing students' information makes the problem more complicated. This student information can provide a useful database for analytical analysis. Methods such as big data analytics and data mining techniques can be deployed to gain insights and pattern that related to student attrition problem. The objective of this paper (i) review the student attrition in higher education (HE) and the contributing factors; and (ii) review the existing computational model to analyze and predict student attrition in HE.

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Section: Big Data Analyticsmentioning

confidence: 99%

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

Tarmizi¹,

Mutalib²,

Hamid³

et al. 2019

IJMECS

View full text Add to dashboard Cite

show abstract

“…The term Big Data has been used initially by two NASA researchers during 1997 to refer to the visualization challenge for systems with large amount of data sets which are ubiquitous in nature [6]. Big data can be understood as the data which is complex, large in volume, rapid growing with numerous, autonomous and independent data sources [33]. Data has increased in various fields on a large scale basis from couple of decades, because of which the term "Big Data" has been coined [20].…”

Section: Introductionmentioning

confidence: 99%

“…[30][31][32][33][34][35][36][37][38][39] validation of data and others are really challenging to achieve the completeness of all dimensions of Big data for SCM.III. DATA ANALYTICS AND TECHNIQUES USED IN SUPPLY CHAIN MANAGEMENT…”

mentioning

confidence: 99%

A Review on Data Analytics for Supply Chain Management: A Case study

Anitha¹,

Patil²

2018

IJIEEB

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The present study bridges the gap between the two intersecting domains, data science and supply chain management. The data can be analyzed for inventory management, forecasting and prediction, which is in the form of reports, queries and forecasts. Because of the price, weather patterns, economic volatility and complex nature of business, the forecasts may not be accurate. This has resulted in the growth of Supply chain analytics. It is the application of qualitative and quantitative methods to solve relevant problems and to predict the outcomes by considering quality of data. The issues like increased collaboration between companies, customers, retailers and governmental organizations, companies are adopting Big Data solutions. Big Data applications can be linked for Supply Chain Management across the fields like procurement, transportation, warehouse operations, marketing and also for smart logistics. As supply chain networks becoming vast, more complex and driven by demands for more exacting service levels, the type of data that is managed and analyzed also becomes more complex. The present work aims at providing an overview of adoption of capabilities of Data Analytics as part of a "next generation" architecture by developing a linear regression model on a sales-data. The paper also covers the survey of how big data techniques can be used for storage, processing, managing, interpretation and visualization of data in the field of Supply chain.

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“…Several techniques are to be followed [4][5][6][7][8] to increase the accuracy, cost reduction and improve the efficiency of big data. Big data uses the traditional classification algorithms, such as decision trees, support vector machine, Naive Bayes neutral network, and k Nearest Neighbors (kNN), Differential Evolution(DE) [9] algorithm, Machine Learning [10] algorithm, Big Data Analytics(BDA) [11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Each tree is fully grown and unpruned based on the best split. Thus using equation(10) &(11) with various decision trees the random forest model is generated. In accordance with the test sample the best class is predicted based on the majority voting.1) Estimating the test error: While growing trees the test error is estimated.…”

mentioning

confidence: 99%

Medical Big Data Classification Using a Combination of Random Forest Classifier and KMeans Clustering

Kumar¹,

Manikandan²

2018

IJISA

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An efficient classification algorithm used recently in many big data applications is the Random forest classifier algorithm. Large complex data include patient record, medicine details, and staff data etc., comprises the medical big data. Such massive data is not easy to be classified and handled in an efficient manner. Because of less accuracy and there is a chance of data deletion and also data missing using traditional methods such as Linear Classifier K-Nearest Neighbor, Random Clustering K-Nearest Neighbor. Hence we adapt the Random Forest Classification using K-means clustering algorithm to overcome the complexity and accuracy issue. In this paper, at first the medical big data is partitioned into various clusters by utilizing k-means algorithm based upon some dimension. Then each cluster is classified by utilizing random forest classifier algorithm then it generating decision tree and it is classified based upon the specified criteria. When compared to the existing systems, the experimental results indicate that the proposed algorithm increases the data accuracy.

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Big Data Analytics for Medical Applications

Abstract: Abstract-BigData is an accumulation of data sets which are abundant and intricate in character. They comprise both structured and unstructured data that evolve abundant, so speedy they are not convenient by classical relational database systems or current analytical tools.

Cited by 33 publications

References 29 publications

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

A Review on Student Attrition in Higher Education Using Big Data Analytics and Data Mining Techniques

A Review on Data Analytics for Supply Chain Management: A Case study

Medical Big Data Classification Using a Combination of Random Forest Classifier and KMeans Clustering

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