Vol. 1 No. 2 (2023): July/December - Issue - 02
Articles

Anaemia Estimation for Patients Using Lasso And Ridge Regression Algorithms

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  • Ambika B J,
  • Nirmala S Guptha,
  • Syeda Ayesha Siddiqha
Ambika B J
Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy for Higher Education, India
Nirmala S Guptha
Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering, Bengaluru, India
Syeda Ayesha Siddiqha
Department of Computer Science and Engineering, HKBK College of Engineering, Bengaluru, India
DOI: https://doi.org/10.5281/zenodo.10255349

Published 2023-12-04

Keywords

  • Anaemic,
  • machine learning,
  • Lasso,
  • Ridge,
  • Anaemic detection

How to Cite

Ambika B J, Nirmala S Guptha, & Syeda Ayesha Siddiqha. (2023). Anaemia Estimation for Patients Using Lasso And Ridge Regression Algorithms. Milestone Transactions on Medical Technometrics, 1(2), 53–63. https://doi.org/10.5281/zenodo.10255349

Abstract

Treatment Suggested by computer opinion is valuable in medical decision-making, saves time, is more accurate, and doesn't require hiring new workers. Numerous nutritional assessments reveal that roughly 25% of people worldwide are anaemic. A machine learning regression that can accurately detect anaemic is therefore urgently needed. To recognise anaemic, it is important to know which classifier, or combination of classifiers, produces the best level of delicacy in the classification of red blood cells. To determine and compute the anaemic, we employed the Lasso and Ridge regressions. However, the Ridge classifier outperforms the Lasso regression and reaches a higher level of delicacy. Consequently, a better and more significant method should be applied to obtain the greatest degree of finesse in medicine.
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