Total views : 265

Aggregated K Means Clustering and Decision Tree Algorithm for Spirometry Data

Affiliations

  • Department of Information and Technology, School of Computing Sciences, Vels University, P.V. Vaithiyalingam Road, Pallavaram, Chennai - 600117, Tamil Nadu, India

Abstract


Objectives: The present research work generally focuses on predicting diseases from the lung disease test by using data mining techniques for spirometry data. Methods/Statistical Analysis: Spirometry is used to create baseline lung function, check out dyspnea, disclose pulmonary disease, watching effects of therapies used to treat respiratory disease, calculate respiratory impairment, evaluate operative risk, and performs surveillance for occupational-relevant lung diseases. Pulmonary function tests are used to find out lung capacity, based on which the many of the lung diseases can be identified. In this research work, a combination of k-means clustering algorithm and Decision tree algorithm was developed. From the results investigation, it is known that the proposed aggregated k-means algorithm and decision tree algorithm for spirometry data is better which compared to other algorithms such as Genetic algorithm, classifier training algorithm, and neural network based classification algorithms. Findings: Existing algorithms are unable to handle noisy data and also with Failure occurrence for a nonlinear data set. It should not classify the data set based on their input attributes. Prediction is not possible for existing system. Applications/Improvement: Spirometry data which is used to predict the lung capacity using Aggregated K-means and Decision tree algorithm. Our proposed approach is evaluated for each dataset accordingly.

Keywords

Decision Tree, Pulmonary Function Test Means, Spirometry Data.

Full Text:

 |  (PDF views: 239)

References


  • Purusothaman G, Krishnakumari P. A Survey of Data Mining Techniques on Risk Prediction: Heart Disease. Indian Journal of Science and Technology. 2015 June; 8(12):1-5.
  • Soni N, Ganatra A. Categorization of Several Clustering Algorithms from Different Perspective: A Review. International Journal of Advanced Research in Computer Science and Software Engineering. 2012 Aug; 2(8):1-6.
  • Na S, Xumin L, Yong G. Research on k-means Clustering Algorithm. Third International Symposium on Intelligent Information Technology and Security Informatics. 2010; 978-0-7695-4020-7/10.
  • Yadav J, Sharma M. A Review of K-mean Algorithm. International Journal of Engineering Trends and Technology (IJETT). 2013 July; 4(7):2972-75.
  • Venkatesan E, Velmurugan T. Performance Analysis of Decision Tree Algorithms for Breast Cancer Classification. Indian Journal of Science and Technology. 2015 Nov; 8(29):1-8.
  • Srimani PK, Koti MS. Knowledge Discovery in Medical Data by using Rough Set Rule Induction Algorithms. Indian Journal of Science and Technology. 2014 July; 7(7):905-15.
  • Vijayarani S, Sudha S. An Efficient Clustering Algorithm for Predicting Diseases from Hemogram Blood Test Samples. Indian Journal of Science and Technology. 2015 Aug; 8(17):1-8.
  • Kanungo T, David M, Netanyahu NS, Christine D, Piatko, Silverman R, Wu AJ. An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE transactions on pattern analysis and machine intelligence. 2002 July; 24(7):881-92.
  • Chirumamilla V, Sruthi BT, Velpula S, Sunkara I. A Novel approach to predict Student Placement Chance with Decision Tree Induction. International Journal of Science & Technology. 2014; 7(1):78-88.
  • Dharmarajan A, Velmurugan T. Lung Cancer Data Analysis by k-means and Farthest First Clustering Algorithms. Indian Journal of Science and Technology. 2015 July; 8(15):1-8.
  • Zhao ZQ, Vogt KMB, Frerichs I. Customized evaluation software for clinical trials: an example on pulmonary function test with electrical impedance tomography. 978-1-4673-2971-2/13/2013.
  • Yun T, Tengyu H, Bing L, Jing T, Zhongjie Z. Regional Voltage Stability Prediction based on decision tree Algorithm. IEEE International Conference on Intelligent Transportation. 2015 Dec 19-20; 978-1-5090-0464-5/16. DOI: 10.1109/ICITBS.2015.
  • Ghorpade-Aher J, Metre VA. PSO based Multidimensional Data Clustering: A Survey. International Journal of Computer Applications. 2014 Feb; 87(16):1. (0975 – 8887).
  • Lakshmi KR, Krishna VM, Kumar VS. Performance comparison of data mining techniques for prediction and diagnosis of breast cancer disease survivability. Asian Journal of Computer Science and Information Technology. 2013; 3(5):81-7.
  • Suma VR, Renjith S, Ashok S, Judy MV. Analytical Study of Selected Classification Algorithms for Clinical Dataset. Indian Journal of Science and Technology. 2016 Mar; 9(11):1-9.
  • Bharati M Ramageri. Data mining techniques and applications. Indian Journal of Computer Science and Engineering. 2010 Dec; 1(4):301-05.
  • Mlambo N. Data Mining: Techniques, Key Challenges and Approaches for Improvement. International Journal of Advanced Research in Computer Science and Software Engineering. 2016 Mar; 6(3):59-65.
  • Kumari AD, Gunasekhar T. A Reconstruction Algorithm using Binary Transform for Privacy-Preserving Data Mining. Indian Journal of Science and Technology. 2016 May; 9(17):1-5.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.