A NOVEL FRAMEWORK FOR CLASSIFICATION OF HEART ATTACK RECORDS FROM ABC OPTIMIZED CLOUD STORAGE

Shobeetha Manirajan, Dr.A.Shaik Abdul Khadir

Abstract


Cardiac Attacks make worse and threatens the middle aged persons in the world. There are lot of researches are undergoing to make control the cardiac heart attacks. It is difficult to predict the causes and possibilities of heart attacks based on the historical patient records. In data mining, the existing techniques like Decision Tree Classification, Naïve Bayesian Algorithm are used to predict the possibilities of heart attacks and their symptoms. However it works well for the structured data. It will not evaluate the unstructured heterogeneous data from various resources. In this proposed work, the analysis performance should be improved to obtain the accurate results. To produce a mechanism for perform predictive analysis about the medical treatments through unstructured heterogeneous data using K Means clustering and SVM Classification. Here the system retrieves the data from the cloud storage. To optimize the data retrieval, here the framework implemented with the Artificial Bee Colony Optimization Algorithm. This helps to obtain the classified results for the preventive measures through the factors from historical data from various cloud resources. To improve the improvements in the analysis based on the mechanism implementation results should be compared with the existing algorithms using UCI dataset of heart attacks in weka tool.

Keywords


K Means Clustering, Decision Tree Classification, Naïve Bayesian Classification, SVM classification.

Full Text:

PDF

References


Rupali and R.Patil, “Heart Disease Prediction System using Naïve Bayes and Jelinek-mercer smoothing”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.3, No.5, pp. 6787-6792, May 2014.

Hlaudi D Masethe and Mosima a Masethe, “Prediction of Heart Disease using Classification Algorithms”, Proceedings of the world congress on Engineering and Computer Science 2014, 22-24 October 2014.

Santhosh Kumar and G.Sahoo, “Classification of Heart Disease using Naïve Bayes and Genetic Algorithm”, Computational Intelligence in Data Mining, Vol.2, pp.269-282, December 2014.

Garima Singh, Kiran Bagwe, Shivani Shanbhag, Shraddha Singh, Sulochana Devi, “Heart disease prediction using Naïve Bayes”, International research Journal of Engineering and Technology, Vol.04, No.03, March-2017.

W Kuanquan, W Lu, W Dianhui and X Lisheng, “SVM Classification for Discriminating Cardiovascular Disease Patients from Non-cardiovascular Disease Controls using Pulse Waveform Variability Analaysis”, Australian Joint Conference on Artificial Intelligence, pp 109-119.

Mythili T, Dev Mukherji, Nikita Padalia and Abhiram Naidu, “A Heart Disease Prediction Model using SVM Decision Trees-Logistics Regression”, International Journal of Computer Aplications, Vol.68, No.16, April 2013.

Hayashi H, Toribatake Y, Murakami H, Yoneyama T, Watanabe T and Tsuchiya H, “Gait Analysis using a Support Vector Machine for Lumbar Spinal Stenosis”, US National Library of Medicine, Vol.38, No.11, Nov 2015.

Talayeh R, Oleg R, Llya S and N Marko, “Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values”, US National Library of Medicine, Vol.11, No.5, May 2016.

Kin Li, Gaochao Xu, G Zhao, Y Dong and D Wang, “Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization”, Chinagrid Conference Sixth Annual, October 2011.

Medhal A, A El-sisi, Arab R Keshik, F.A.Torkey, “Cloud task scheduling based on ant colony optimization”, International Conf. on Computer Engineering and Systems

Prachi Verma, Sonika shrivastava ad R.K.Pateriya, “Enhancing Load Balancing in Cloud Computing by Ant Colony Optimization Method”, Int. Journal of Compuer Engineering in Research Trends”, Vol. 4, No.6, June 2017, pp.277-284.

S.Saravanan, V.Venkatachalam and S.Then Malligai, “Optimization of SLA Violation in Cloud Computing using Artificial Bee Colony”, Int. Journal of Advances in Engineering, Vol.1, No.3, pp.410-414, March 2015.

Warangkhana Kimpan and Bokhatai Kruekaew, “Heuristic Task Scheduling with Artificial Bee Colony algorithm fro virtual Machines”, IEEE Conf. on Soft Computing an Intelligent Systems, Aug 2016

Shaleen Shukula and Rutvik Mehta, “Artificial Bee Colony Algorithm on Big Data to find out required Data Sources”, International Journal of Research Culture Society, Vol.1, No.3, May 2017.




DOI: https://doi.org/10.26483/ijarcs.v8i9.5034

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 International Journal of Advanced Research in Computer Science