Data mining with Predictive analysis for healthcare sector: An Improved weighted associative classification approach

α
Y.SHIRISHA
Y.SHIRISHA
σ
S.SIVA SHANKAR RAO
S.SIVA SHANKAR RAO
ρ
D. SUJATHA
D. SUJATHA
α Jawaharlal Nehru Technological University, Hyderabad

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Data mining with Predictive analysis for healthcare sector: An Improved weighted associative classification approach

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Abstract

Association mining has seen its growth right through data mining during the last few years as it has the ability to search for that entire database that could be of least constraints associated with it.Thus finding such small database sets could be done with the help of predictive analysis method. The paper enlightens the combinational classification of association and classification data mining. For this to happen a new set of constraints need to be introduced namely classification association rule( CAR).some systems like classification systems with domain experts are the ones that can be associated with. For fields like medicine where a lot many patients consult each doctor, but every patient has got different personal details not necessarily may suffer with same disease. So the doctor may look for a classifier, which could provide all details about every patient and henceforth necessary medications can be provided. However there have been many other classification methods like CMAR, CPAR MCAR and MMA and CBA.Some advance associative classifiers have also seen growth very recently with small amendments in terms of support and confidence, thereby accuracy. In this paper we proposed a HIT algorithm based automated weight calculation approach for weighted associative classifier.

References

11 Cites in Article
  1. Jyothipillai Sunitasoni,O Vyas (2009). An Associative Classifier Using Weighted Association Rule.
  2. Zuoliang Chen,Guoqing Chen (2008). Building an Associative Classifier Based on Fuzzy Association Rules.
  3. M Khan,M Muyeba,Coenen (2008). F A Weighted Utility Framework for Mining Association Rules.
  4. Fadithabtah (2007). A review of associative classification mining.
  5. University Luizaantonie,Alberta Of (2007). Kernel Classifiers from a Machine Learning Perspective.
  6. Feng Tao,Fionn Murtagh,Mohsen Farid (2003). Weighted Association Rule Mining using weighted support and significance framework.
  7. X Yin,J Han (2003). CPAR: Classification based on predictive association rule.
  8. W Li,J Han,J Pei (2001). CMAR: Accurate and efficient classification based on multiple class association rules.
  9. B Liu,W Hsu,Ma (1998). Integrating Clasification and association rule mining.
  10. M Cláudia,Arlindo Antunes,Oliveira Temporal Data Mining: an overview.
  11. M Antonie,O (2004). An associative classifier based on positive and negative rules.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Y.SHIRISHA. 1970. \u201cData mining with Predictive analysis for healthcare sector: An Improved weighted associative classification approach\u201d. Unknown Journal GJCST Volume 11 (GJCST Volume 11 Issue 22): .

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GJCST Volume 11 Issue 22
Pg. 31- 36
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v1.2

Issue date

January 12, 2012

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en
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Association mining has seen its growth right through data mining during the last few years as it has the ability to search for that entire database that could be of least constraints associated with it.Thus finding such small database sets could be done with the help of predictive analysis method. The paper enlightens the combinational classification of association and classification data mining. For this to happen a new set of constraints need to be introduced namely classification association rule( CAR).some systems like classification systems with domain experts are the ones that can be associated with. For fields like medicine where a lot many patients consult each doctor, but every patient has got different personal details not necessarily may suffer with same disease. So the doctor may look for a classifier, which could provide all details about every patient and henceforth necessary medications can be provided. However there have been many other classification methods like CMAR, CPAR MCAR and MMA and CBA.Some advance associative classifiers have also seen growth very recently with small amendments in terms of support and confidence, thereby accuracy. In this paper we proposed a HIT algorithm based automated weight calculation approach for weighted associative classifier.

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Data mining with Predictive analysis for healthcare sector: An Improved weighted associative classification approach

Y.SHIRISHA
Y.SHIRISHA Jawaharlal Nehru Technological University, Hyderabad
S.SIVA SHANKAR RAO
S.SIVA SHANKAR RAO
D. SUJATHA
D. SUJATHA

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