Precision healthcare analytics: Integrating ML for automated image interpretation, disease detection, and prognosis prediction
1 Department of International Relations, Federal University of Lafia. Nigeria.
2 Department of Procurement, Clarissa Dynamic Links Ltd, Makurdi, Nigeria.
3 Department of Production, Von Food and Farms limited, Nimo, Anambra, Nigeria.
4 Department of Network Infrastructure Building, VEA, Telecoms, Manchester, United Kingdom.
5 Department of Electrical/Electronic Engineering, University of Port Harcourt, Nigeria.
6 Department of Telecommunications, Enforcement Ancillary and Maintenance, National Broadcasting Commission Headquarters, Aso-Villa, Abuja, Nigeria.
Review
World Journal of Biology Pharmacy and Health Sciences, 2024, 18(01), 336–354.
Article DOI: 10.30574/wjbphs.2024.18.1.0214
Publication history:
Received on 10 March 2024; revised on 20 April 2024; accepted on 23 April 2024
Abstract:
This review paper provides an overview of precision healthcare analytics, focusing on the integration of machine learning (ML) techniques for automating image interpretation, disease detection, and prognosis prediction across various medical imaging modalities, including X-rays, MRIs, and CT scans. Drawing upon existing literature and empirical evidence, we assess the impact of ML-driven automated image interpretation on diagnostic accuracy, highlighting its superiority over traditional methods. Additionally, we examine the effectiveness of ML algorithms in disease detection, emphasizing their potential for early intervention and improved patient outcomes. Furthermore, we explore the prognostic capabilities of ML-based models in forecasting disease progression and guiding treatment strategies. Through a comprehensive synthesis of research findings, we identify key factors influencing the performance of ML algorithms in healthcare applications and discuss strategies for addressing challenges related to data quality, interpretability, and scalability. By critically evaluating current trends and advancements in precision healthcare analytics, this review aims to provide insights into the potential benefits and limitations of ML integration in medical practice, contributing to the ongoing discourse on enhancing patient care and healthcare delivery.
Keywords:
Healthcare; Machine Learning; Medical Imaging; Disease Detection; Data Analytics
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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