Machine learning's role in personalized medicine & treatment optimization
1 Department of Information Technology, University of the Cumberlands, KY, USA.
2 Department of Computer Science, San Francisco Bay University, CA, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 21(02), 1675–1686
Article DOI: 10.30574/wjarr.2024.21.2.0641
Publication history:
Received on 13 January 2024; revised on 22 February 2024; accepted on 24 February 2024
Abstract:
The advent of machine learning in personalized medicine has revolutionized the healthcare industry by providing an enhanced diagnosis and treatment regimen to patients based on their unique characteristics such as genetic predispositions, lifestyle variables, and medical history. Machine learning algorithms can analyze vast amounts of patient data to generate accurate diagnoses, establish tailored treatment plans, and improve patient outcomes. By combining multiple data sources, machine learning algorithms can identify patterns, predict the likelihood of specific illnesses, and recommend personalized treatment options. The technology has enabled healthcare professionals to access diverse datasets, including genetic information, medical history, and lifestyle variables, and derive insights from them that were previously inaccessible. However, the isolation of user data in silos across multiple hospitals and medical institutions presents challenges for researchers working in this sector. The article examines the possible obstacles and ethical implications associated with the widespread implementation of machine learning in personalized medicine and assesses the consequences of these breakthroughs for patient care, healthcare systems, and the future of medical research.
Keywords:
Healthcare; Machine Learning Algorithm; Decision Tree; Random Forest; Deep Learning; IoT
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0