We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Dimensionality Reduction of Healthcare Data through Niche Genetic Algorithm

by Madhu H.K., D. Ramesh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 53
Year of Publication: 2022
Authors: Madhu H.K., D. Ramesh
10.5120/ijca2022921945

Madhu H.K., D. Ramesh . Dimensionality Reduction of Healthcare Data through Niche Genetic Algorithm. International Journal of Computer Applications. 183, 53 ( Feb 2022), 7-11. DOI=10.5120/ijca2022921945

@article{ 10.5120/ijca2022921945,
author = { Madhu H.K., D. Ramesh },
title = { Dimensionality Reduction of Healthcare Data through Niche Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 53 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number53/32287-2022921945/ },
doi = { 10.5120/ijca2022921945 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:19:47.790735+05:30
%A Madhu H.K.
%A D. Ramesh
%T Dimensionality Reduction of Healthcare Data through Niche Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 53
%P 7-11
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Technology into medical health care has generated voluminous parameters for human physiological condition, forming data for high dimensions. Data which is raw makes any computing techniques complex and it is not structured.Structuring data can be a pre-processing model,but extracting useful parameters which contribute to reducing the computational complexities of any intelligent algorithm for classification and prediction is a big challenge in technology. Dimensionality reduction is a common strategy adopted by research to select appropriate parameters for further computations. In this research work Niche genetic algorithm is implemented on various healthcare datasets which extracts relevant parameters for classification and prediction of healthcare data with reduced computation complexity and increased accuracy. The proposed model is independent of any application, but restricts to structured data.

References
  1. G. Thippa Reddy, Praveen Kumar Reddy, KuruvaLakshmanna, Rajesh Kaluri, Dharmendra Singh Rajput, Gautam Srivastava andThar Baker. “Analysis of Dimensionality Reduction Techniques on Big Data”. https://creativecommons.org/lic. VOLUME 8, 2020. IEEE Access.
  2. Beatriz Remeseiroa and Veronica Bolon-Canedo. “A review of feature selection methods in medical applications”. 2019 Elsevier Ltd. Computers in Biology and Medicine 112 (2019) 103375.
  3. Shaeela Ayesha, Muhammad Kashif Hanif and Ramzan Talib. “Overview and comparative study of dimensionality reduction techniques for high dimensional data”. Information Fusion 59 (2020) 44–58. 2020 Elsevier.
  4. RazanAbdulhammed, Hassan Musafer, Ali Alessa and MiadFaezipour. “Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection”. Electronics 2019, 8, 322; www.mdpi.com/journal/electronics.
  5. Yashar Kiarashinejad, Sajjad Abdollahramezani and Ali Adibi. “Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures”. School of Electrical and Computer Engineering, Georgia Institute of Technology, 778 Atlantic Drive NW, Atlanta, GA 30332, USA.
  6. Rizgar R. Zebari, Adnan Mohsin Abdulazeez, Diyar QaderZeebaree, Dilovan Asaad Zebari and Jwan Najeeb Saeed. “A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction”. Journal of Applied Science and Technology Trends Vol. 01, No. 02, pp. 56 –70, (2020).
  7. Xubin Wang, Yunhe Wang, Ka-Chun Wong and Xiangtao Li. “A self-adaptive weighted differential evolution approach for large-scale feature selection”. https://doi.org/10.1016/j.knosys.2021. 07633 0950-7051/© 2021 Elsevier.
  8. Gurcan Yavuz and Dogan Aydın. “Improved Self-adaptive Search Equation-based Artificial Bee Colony Algorithm with competitive local search strategy”. https://doi.org/10.1016/j.swevo.2019.100582. 11 October 2019.
  9. Qianqian Zhang, Daqing Wang and Lifu Gao. “Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm”. International Journal of Advanced Robotic Systems. May-June 2021: 1–11 DOI: 10.1177/17298814211014413. journals.sagepub.com/home/arx.
  10. Shanshan Guo, Hongliang He and Xiaoling Huang. “A Multi-Stage Self-Adaptive Classifier Ensemble Model With Application in Credit Scoring”. 2169-3536. 2019 IEEE. http://www.ieee.org/publications_standards/publications/rights/index.html.
  11. Kusum Kumari Bharti and P.K. Singh. “A three-stage unsupervised dimension reduction method for text clustering”. Journal of Computational Science 5 (2014) 156–169. http://dx.doi.org/10.1016/j.jocs.2013.11.007.
  12. D.Napoleon and S.Pavalakodi . “A New Method for Dimensionality Reduction using KMeans Clustering Algorithm for High Dimensional Data Set”. International Journal of Computer Applications (0975 – 8887). Volume 13– No.7, January 2011.
  13. Krzysztof Siwek, StanisławOsowski, Tomasz Markiewicz and Jacek korytkowski. “Analysis of medical data using dimensionality reduction techniques”. PrzeglądElektrotechniczny, ISSN 0033-2097, R. 89 NR 2a/2013.
  14. Rahmat Widia Sembiring, Jasni Mohamad Zain and Abdullah Embong. “Dimension Reduction of Health Data Clustering”. IJNCAA, 2011 (ISSN: 2220-9085).
  15. Min Zhu, Jing Xia, Molei Yan, Guolong Cai, Jing Yan and Gangmin Ning. “Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm”. Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine. Volume 2015, Article ID 794586, http://dx.doi.org/10.1155/2015/794586.
  16. Basna Mohammed Salih Hasan and Adnan Mohsin Abdulazeez. “A Review of Principal Component Analysis Algorithm for Dimensionality Reduction”. Journal Of Soft Computing and Data Mining. VOL. 2 NO. 1 (2021) 20-30.
  17. ErsinKusetBodur and Donald Douglas. “Filter Variable Selection Algorithm Using Risk Ratios for Dimensionality Reduction of Healthcare Data for Classification”. Processes 2019, 7, 222; doi:10.3390/pr7040222. www.mdpi.com/journal/process.
  18. Divya Jain and Vijendra Singh. “An efficient hybrid feature selection model for dimensionality reduction”. ICCIDS 2018. Procedia Computer Science 132 (2018) 333–341.
  19. S.Velliangiria, S.Alagumuthukrishnan and S IwinThankumar joseph. “A Review of Dimensionality Reduction Techniques for Efficient Computation”. ICRTAC 2019.
  20. A. E. I. Brownlee, O. Regnier-Coudert, J. A. McCall, S. Massie, and S. Stulajter, “An application of a GA with Markov network surrogate to feature selection,” International Journal of Systems Science, vol. 44, no. 11, pp. 2039–2056, 2013.
  21. C. W. Ho, K. H. Lee, and K. S. Leung, “A genetic algorithm based on mutation and crossover with adaptive probabilities,” in Proceedings of the Congress on Evolutionary Computation (CEC ’99), vol. 1, IEEE, Washington, DC, USA, July 1999.
  22. T. Ingu and H. Takagi, “Accelerating a GA convergence by fitting a single-peak function,” in Proceedings of the IEEE International Fuzzy Systems Conference, vol. 3, pp. 1415–1420, Seoul, Republic of Korea, August 1999.
  23. UCI Heart disease data set. https://archive.ics.uci.edu/ l/datasets/Heart+Disease.
Index Terms

Computer Science
Information Sciences

Keywords

Dimensionality reduction Principal Component Analysis (PCA) UCI Health care dataset.