Year: 2025
Type: article
Study design: Observational Study
Abstract: Random Forest machine learning model could be used to classify hypertension using BMI and regular laboratory characteristics. The overall number of participants included in the analysis was 150 and multiple models were trained on key clinical variables including fasting glucose, LDL and BMI. The Random Forest model revealed the best predictive accuracy of 88.0%. Thus, we show the possibility of applying non-invasive poor data to early detection of hypertension. According to the study, machine learning could increase the effectiveness of screening and patient outcomes.
Source: Bioinformation
Authors: Amisha Sood, Sangeeta Gupta, Shweta Ramnarayan Borkar, Amrit Podder, Parth Jani
Institutions: Wrexham Maelor Hospital, All India Institute of Medical Sciences Gorakhpur, Datta Meghe Institute of Higher Education and Research, Teerthanker Mahaveer University, Teerthanker Mahaveer Medical College & Research Centre, Mahaveer University, All India Institute of Medical Sciences, Rajkot
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4415944983