Multi-label Associative Classification of Medical Documents from MEDLINE

Text classification in medical domain could result in an easier way of handling large volumes of medical data. They can be segregated depending on the type of diseases, which can be determined by extracting the decisive key texts from the original document. Due to various nuances present in understanding language in general, a requirement of large volumes of text-based data is required for algorithms to learn patterns properly. The problem with existing systems such as MedScape, MedLinePlus, Wrappin, and MedHunt is that they involve human interaction and high time consumption in handling a large volume of data. By employing automation in this proposed field, the large involvement of manpower could be removed which in turn speeds up the process of classification of the medical documents by which the shortage of medical technicians in third world countries are addressed.

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A MapReduce solution for associative classification of big data

Information Sciences ◽

10.1016/j.ins.2015.10.041 ◽

2016 ◽

Vol 332 ◽

pp. 33-55 ◽

Cited By ~ 66

Author(s):

Alessio Bechini ◽

Francesco Marcelloni ◽

Armando Segatori

Keyword(s):

Big Data ◽

Associative Classification

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Associative classification of the Jordanian hospitals efficiency based on DEA

Global Journal of Computer Sciences Theory and Research ◽

10.18844/gjcs.v8i3.4022 ◽

2018 ◽

Vol 8 (3) ◽

pp. 120-125

Author(s):

Ahmad Alaiad ◽

Hassan Najadat ◽

Nusaiba Al-Mnayyis ◽

Ashwaq Khalil

Keyword(s):

Data Mining ◽

Decision Makers ◽

Healthcare Sector ◽

Data Envelopment ◽

Associative Classification ◽

Dea Model ◽

Knowledge Analysis ◽

Minimum Number ◽

And Performance

Data envelopment analysis (DEA) has been widely used in many fields. Recently, it has been adopted by the healthcare sector to improve efficiency and performance of the healthcare organisations, and thus, reducing overall costs and increasing productivity. In this paper, we demonstrate the results of applying the DEA model in Jordanian hospitals. The dataset consists of 28 hospitals and is classified into two groups: efficient and non-efficient hospitals. We applied different association classification data mining techniques (JCBA, WeightedClassifier and J48) to generate strong rules using the Waikato Environment for Knowledge Analysis. We also applied the open source DEA software and MaxDEA software to manipulate the DEA model. The results showed that JCBA has the highest accuracy. However, WeightedClassifier method achieves the highest number of generated rules, while the JCBA method has the minimum number of generated rules. The results have several implications for practice in the healthcare sector and decision makers. Keywords: Component, DEA, DMU, output-oriented model, health care system.

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