Comparative Analysis and Prediction of Malignant Tumor Mortality in China Based on LSTM Models

Fan Yang

2023

Abstract

The prediction and analysis of the mortality rate of malignant tumors is a hot topic of great importance and urgency in the world. However, the dynamic mortality prediction model commonly used in the medical field still has some limitations in the nonlinear structure of mortality research. Therefore, based on the survey data of "Mortality rate of major diseases in selected urban/rural areas of China" from 2008 to 2021, this research will utilize the Long and short-term memory networks (LSTM) model to validate the model and predict the mortality rate in 2022. This research will also quantitatively analyze and explore the reasons for the mortality rate differences arising from regional differences and gender differences. The prediction results show that the mortality rate in urban areas in 2022 may increase compared with the value in 2021, while the mortality rate in rural areas will decrease, and the difference in the trend is only reflected in the regional differences. Furthermore, the trends in mortality rates over the years show a general decline in urban areas and an increase in rural areas, with the urban mortality rate being lower than the rural rate after 2020, and the male mortality rate being much higher than the female mortality rate.

Paper Citation

in Harvard Style

Yang F. (2023). Comparative Analysis and Prediction of Malignant Tumor Mortality in China Based on LSTM Models. In Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-705-4, SciTePress, pages 178-183. DOI: 10.5220/0012816200003885

in Bibtex Style

@conference{daml23,
author={Fan Yang},
title={Comparative Analysis and Prediction of Malignant Tumor Mortality in China Based on LSTM Models},
booktitle={Proceedings of the 1st International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2023},
pages={178-183},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012816200003885},
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 - Comparative Analysis and Prediction of Malignant Tumor Mortality in China Based on LSTM Models
SN - 978-989-758-705-4
AU - Yang F.
PY - 2023
SP - 178
EP - 183
DO - 10.5220/0012816200003885
PB - SciTePress