Generalized feature transformative dependency magnitude decision tree classification for COVID-19 medical data analysis

Authors

  • A. Mary Theresa Assistant Professor, Department of Information Technology, Nirmala College for Women, Coimbatore - 641 018. Tamil Nadu, India
  • V. Saravanan Professor & HEAD, Department of Information Technology, Hindusthan College of Arts and Science, Coimbatore - 641 028. Tamil Nadu, India

Keywords:

big data analytics, COVID-19 prediction, hosmer–lemeshow, log transform-based feature, engineering model, hellinger generalized, multidimensional scaling, feature map, rand dependency magnitude, iterative dichotomiser-3, decision tree classification model

Abstract

Data mining is a method for identifying attractive patterns in an understandable format from big data. Big data mining is an integration of structured and unstructured data that are mined for extracting valuable information and used for predictive analytics. Big data analytics are used in many applications, especially in the healthcare sector.  With a variety of data analytics devices and procedures, the healthcare domain uses big data to update health prevention and management. Big data analytics for healthcare create it feasible to invent smarter evaluations of patient heath conditions. One of the most current and relevant big data analytics in healthcare is the global COVID-19 prediction. Many works have been introduced for COVID-19 prediction with big data analytics.   But, the precision results and time consumption was not focused on by existing methods. Therefore, a novel Log Transformative Generalized feature Mapping based Rand Dependency Magnitude Iterative Dichotomiser-3 Decision Tree Classification (LTGFM-RDMDDTC) is introduced for COVID-19 prediction with higher accuracy, precision, and lesser time consumption. In medical data analytics, the performance of the model is said to be improved by including three major processes namely feature engineering, feature selection, and classification. 

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Published

19-10-2022

How to Cite

Theresa, A. M., & Saravanan, V. (2022). Generalized feature transformative dependency magnitude decision tree classification for COVID-19 medical data analysis. International Journal of Health Sciences, 6(S10), 429–445. Retrieved from https://sciencescholar.us/journal/index.php/ijhs/article/view/13493

Issue

Vol. 6 No. S10 (2022)

Section

Peer Review Articles
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