Advanced machine learning algorithms for blood pressure classification: Early detection or prevention could save lives

Aug 2, 2026

The study applies advanced machine learning techniques to classify blood pressure levels and support predictive decision-making. It evaluates Naïve Bayes, AdaBoost, feedforward neural networks, and long short-term memory models against a conventional multinomial logistic approach using accuracy, F1-score, kappa, sensitivity, specificity, and AUC metrics. Results identify feedforward neural networks as the top-performing model while highlighting LSTM’s ability to capture temporal patterns. The findings indicate that age, anthropometrics, lifestyle, and fitness variables can meaningfully inform hypertension prevention, detection, and management.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Objectives

Methods

Results

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Keywords

Introduction

[https://intjhs.org/](https://intjhs.org/) International Journal of Health Sciences Original Article Advanced machine learning algorithms for blood pressure classification: Early detection or prevention could save lives Jonathan Iworiso, PhD1* , Bari-ika Nornubari Vite, PhD2, Itunu Godwin Osuntoki, PhD3, Idorenyin A. Amaunam, MSc4, Idris Olayiwola Olawale, MSc1, Saksham Arora, MSc1 1School of Computing and Digital Media, London Metropolitan University, London, 2Department of Applied Social Sciences/Health and Social Care, University of Bedfordshire, Milton Keynes, 3Department of Statistics, Modelling and Economics, UK Health Security Agency, London, 4School of Computer Science, University of Essex, Colchester, United Kingdom. *Corresponding author: Jonathan Iworiso, School of Computing and Digital Media, London Metropolitan University, London, United Kingdom. [j.iworiso@londonmet.ac.uk](j.iworiso@londonmet.ac.uk) | Received: 16 November 2024
Accepted: 28 April 2025
Published: 01 July 2025 | | --- | | DOI
10.25259/OJS_8819 | Quick Response Code: ABSTRACT Objectives: The primary objective of the study is to classify the blood pressure (BP) levels using advanced machine learning (ML) techniques for predictive purposes. The study assesses the efficacy of the Naïve Bayes, AdaBoost, feedforward neural networks (FNNs), and long short-term memory (LSTM) algorithms over the conventional multinomial logistic model using standard performance evaluation metrics. Methods: The dataset comprised 15,000 entries obtained from the National Health Service, England, each containing eight variables. The variables include BP, age, weight, height, gender, smoking habit, alcohol consumption, and fitness level. The Naïve Bayes, AdaBoost, FNN, LSTM, and multinomial logistic models were employed in the study. Each model underwent training, testing, validation, and evaluation using suitable metrics such as accuracy, F1-Score, kappa statistics, sensitivity, specificity, and area under the curve score. Results: The FNN model gives the highest test accuracy of 89.47% and balanced performance, making it the most appropriate model for predicting BP levels. The LSTM model demonstrated strong proficiency in capturing temporal patterns. AdaBoost was highly effective for dealing with class imbalance, but Naïve Bayes was a dependable benchmark. The multinomial logistic model established a reliable and stable reference point. The results represented a notable improvement over previous research, which typically reported median accuracy rates in the 80–85% range. Conclusion: The study reveals that knowing an individual’s age, weight, height, gender, smoking habit, alcohol consumption, and fitness level is useful in predicting his/her BP level. Thus, the advanced ML algorithms demonstrate potential in accurately classifying BP levels and can aid in the prevention, detection, and management of hypertension. Keywords: AdaBoost, Blood pressure, Feedforward neural network, Long short-term memory, Multinomial, Naïve Bayes INTRODUCTION The categorization of blood pressure (BP) is a crucial component of clinical research. BP measurements, acquired during physical examinations, outpatient appointments, or hospital How to cite this article: Iworiso J, Vite BN, Osuntoki IG, Amaunam IA, Olawale IO, Arora S. Advanced machine learning algorithms for blood pressure classification: Early detection or prevention could save lives. Int J Health Sci (Qassim) . 2025;19:31-42 . doi: 10.25259/OJS_8819 This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. ©2025 Published by Scientific Scholar on behalf of International Journal of Health Sciences ~~ ~~International~~ ~~Journal~~ ~~of

FAQs

Which machine learning models were used to classify blood pressure levels?

The study evaluates Naïve Bayes, AdaBoost, feedforward neural networks (FNN), and long short-term memory (LSTM) alongside a conventional multinomial logistic model.

What dataset was used and what variables does it contain?

The dataset includes 15,000 entries from the National Health Service, England, with eight variables: blood pressure, age, weight, height, gender, smoking habit, alcohol consumption, and fitness level.

Which model performed best and what did the results indicate for prediction?

The FNN model achieved the highest test accuracy (89.47%) with balanced performance. Overall, advanced ML models can accurately classify blood pressure levels and help support early detection and prevention of hypertension.

Aug 2, 2026

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