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A Review on Prediction of Multiple Diseases and Performance Analysis using Data Mining and Visualization Techniques

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Ajinkya Kunjir, Harshal Sawant, Nuzhat F. Shaikh
10.5120/ijca2016912256

Ajinkya Kunjir, Harshal Sawant and Nuzhat F Shaikh. A Review on Prediction of Multiple Diseases and Performance Analysis using Data Mining and Visualization Techniques. International Journal of Computer Applications 155(1):34-38, December 2016. BibTeX

@article{10.5120/ijca2016912256,
	author = {Ajinkya Kunjir and Harshal Sawant and Nuzhat F. Shaikh},
	title = {A Review on Prediction of Multiple Diseases and Performance Analysis using Data Mining and Visualization Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {1},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {34-38},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume155/number1/26572-2016912256},
	doi = {10.5120/ijca2016912256},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In the field of medical science a tremendous amount of data is generated, doctors need to test the patient physically to find out the injuries and disease of the patient. This paper outlines the idea of predicting a particular disease by performing operations on the digital data generated in the medical diagnosis. In this project an efficient genetic algorithm hybrid with the techniques like back propagation and Naive Bayes approach for disease prediction is proposed. Bad clinical decisions would cause death of a patient which cannot be afforded by any hospital. To achieve a correct and cost effective treatment, computer technology Systems can be developed to make good decision. There is a lot of medical information unexplored, which gives rise to an important query of how to make useful information out of the data. The purpose of this project is to construct a basic prototype model which can determine and extract unknown knowledge (patterns, concepts and relations) related with multiple disease from a past database records of specified multiple diseases. It can solve complicated queries for detecting a particular disease and thus assist medical practitioners to make intelligent clinical decisions which traditional decision support systems were not able to. By providing efficient treatments, it can help to reduce costs of treatment. The medical organizations are ”rich in data” but their ”knowledge utilization is poor ”. There is a lack of sufficiency of improved analysis techniques to find relations, concepts and patterns in the medical data. Data mining is science and engineering study of extracting previously undiscovered patterns from a huge set of data. In this paper, data mining methods namely, Decision tree, Naïve Bayes, and Back-Propagation(ANN) algorithms are implemented on medical data sets .The medical datasets will be represented graphically(graphs , charts , shapes )using different visualization techniques. The algorithms are compared and evaluated on basis of their accuracy and time consumption factors. The algorithm which gives out high accuracy and less duration to give the output is analysed and implemented.

References

  1. Ankita Dewan, Meghna Sharma , ”Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification” , 2015 2ndInternationalConference on Computing for Sustainable Global Development (INDIACom), IEEE 2015.
  2. HICAP:Hierarchial Clustering with Pattern Preservation (2004). Hui Xiong, Michael Steinbach, Pang-Ning Tan, and Vipin Kumar, In Proc. of the Fourth SIAM International Conf. on Data Mining (SDM’04), Florida, USA, 2004.
  3. Jyoti Soni, Ujma Ansari, Dipesh Sharma, Sunita Soni , ”Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction” , International Journal of Computer Applications (0975 8887) Volume 17 No.8, March 2011.
  4. Abhishek Taneja. ”Prediction of heart diseases using data mining techniques”. Oriental Journal of computer science and technology. December 2013. Vol. 6, No. (4): Pgs. 457-466.
  5. T. Revathi S. Jeevitha, ”Comparative Study on Heart Disease Prediction System Using Data Mining Techniques ”,International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2013)
  6. Monika Gandhi, Dr.Shailendra Narayan Singh ,”Predictions in Heart Disease Using Techniques of Data Mining” , 2015 1st International Conference on Futuristic trend in Computational Analysis and Knowledge Management (ABLAZE-2015), IEEE 2015.
  7. Amrender Kumar, ”ARTIFICIAL NEURAL NETWORKS FOR DATA MINING” ,I.A.S.R.I., Library Avenue, Pusa, New Delhi-110 012.
  8. Nuzhat F. Shaikh, Dharmpal D. Doye,”An Adaptive Central Force Optimization (ACFO) and Feed Forward Back Propagation Neural Network (FFBNN) based iris recognition system”, Journal of Intelligent and Fuzzy Systems 30 (2016) 20832094 DOI:10.3233, IOS Press,2083.
  9. R. Spence, L. Tweedie, H. Dawkes, and H. Su, ”Visualization for functional design”, in Proc. Int. Symp. on Information Vi- sualization (InfoVis 95), 1995, pp. 410
  10. Nuzhat F. Shaikh, Dharmpal D. Doye, "Improving the Accuracy of Iris Recognition System using Neural Network and Particle Swarm Optimization"International Journal of Computer Applications (0975 – 8887)Volume 79 – No3, October 2013 

Keywords

Prediction, Classification, BP Neural networks, Genetic algorithms, Decision Tree, Regression, Naive Bayes.