https://milestoneresearch.in/JOURNALS/index.php/TMT/issue/feed 2023-12-04T12:05:32+00:00 Dr. Syed Thouheed Ahmed editor_technometrics@milestoneresearch.in Open Journal Systems <p><strong><em>Milestone Transactions on Medical Technometrics</em> [ISSN:</strong> <strong>2584-072X</strong>] is a medical journal dedicated towards technological advancements in biomedical sciences within the domain of engineering and technological innovations. Milestone Transactions on Medical Technometrics invites researchers to submit novel and unpublished research and surveys. The journal includes the aspects of biomedical innovations and research using computer science and engineering domains such as artificial intelligence (AI), machine learning (ML), intelligent communication, data processing, human computer interaction (HCI) systems and much more.</p> https://milestoneresearch.in/JOURNALS/index.php/TMT/article/view/126 2023-12-04T12:05:32+00:00 Ambika B J a@gmail.com Nirmala S Guptha a@gmail.com Syeda Ayesha Siddiqha a@gmail.com

<div><span lang="EN-IN">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.</span></div>

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