An ML Framework for the Early Detection and Prediction of Hypertension: Enhancing Diagnostic Accuracy
Year: 2025
Type: conference-paper
Abstract: A major worldwide health problem, hypertension can result in serious consequences such as stroke, renal failure, and cardiovascular illnesses if it is not identified and treated promptly. Reducing death rates and facilitating prompt therapies need the early identification of hypertension. This research examines if there are ways ML could enhance early identification of hypertension. Therefore, hypertension is still considered a global public health problem, and one of the most important preventive goals is its timely and accurate diagnosis. Leveraging a 99.92% accuracy rate, the present study therefore proposes a novel ML framework that significantly dwarfs the currently documented best accuracy of 99.5%. This achievement of correctly identifying the essentiality of hypertension in establishing our recommended paradigm highlights the robustness and trustworthiness of the proposed actions to ensure timely treatment and enhance patients’ quality of life the largest amount.
Institutions: University of Sialkot, Nusa Putra University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4413722362