Use of machine learning to predict hypertension based on BMI and routine lab data
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.
Authors
- Piyush Jani
- Amrit Podder (ORCID: https://orcid.org/0000-0002-9816-3347)
- Sangeeta Gupta (ORCID: https://orcid.org/0000-0001-7938-4374)
- A. K. Sood (ORCID: https://orcid.org/0000-0002-8652-1389)
- Shekhar Borkar
Institutions
- Baba Raghav Das Medical College (IN)
- Wrexham Maelor Hospital (GB)
- Datta Meghe Institute of Higher Education and Research (IN)
- Teerthanker Mahaveer Medical College & Research Centre (IN)
- All India Institute of Medical Sciences (IN)
- Deen Dayal Upadhyaya Gorakhpur University (IN)
Publication Details
- Journal
- Bioinformation
- Published
- 2025-10-31
- DOI
- https://doi.org/10.6026/973206300213645
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.94