Machine Learning Utilized in Prognosis of Hypertension

Zihan Zhou

2023

Abstract

People are getting increasingly conscious of their physical health issues since their quality of life advances. Computers empower the medical industry, making medicine gradually become visualized. The adverse effects of hypertension in the human body are already well established. As more people become aware of this, they desire to be able to figure out whether or not they have hypertension without consulting a doctor. The development of digital health has given this castle in the sky a foothold on the ground. According to hypertension, there are 13 influencing factors in total: age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal. The method adopted in this article is to use a neural network model with the help of Python. By changing the factor’s weight, the critical alpha factor obtains a higher weight and classifies it more efficiently and accurately. This article chooses a simple neural network model, Multi Layer Perceptron (MLP), then uses the validation set obtained from the data set to optimize hyperparameters and improves it multiple times to obtain suitable hyperparameters to establish an optimal MLP model.

Paper Citation

in Harvard Style

Zhou Z. (2023). Machine Learning Utilized in Prognosis of Hypertension. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 279-284. DOI: 10.5220/0012801300003885

in Bibtex Style

@conference{daml23,
author={Zihan Zhou},
title={Machine Learning Utilized in Prognosis of Hypertension},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={279-284},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012801300003885},
isbn={978-989-758-705-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Machine Learning Utilized in Prognosis of Hypertension
SN - 978-989-758-705-4
AU - Zhou Z.
PY - 2023
SP - 279
EP - 284
DO - 10.5220/0012801300003885
PB - SciTePress