UAA-GAIN

The source code for Towards Sustainable Compressive Population Health: A GAN-based Year-By-Year Imputation Method

Published on ACM Transactions on Computing for Healthcare 2022

Thank you for your interest in our work, we have uploaded the Section 4 DATA OBSERVATION AND INTUITION and all the code for the model here.

Requirements

Install python, tensorflow. We use Python 3.7, Tensorflow 1.14.0. See requirements.txt

Data preparation

The three open-world Chronic Diseases Prevalence Datasets can be downloaded at:

• UK-Obesity Dataset: https://digital.nhs.uk/data-and-information/publications/statistical/quality-and-outcomes-framework-achievement-prevalence-and-exceptions-data
• US-Hypertension Dataset: https://www.cdc.gov/500cities/
• Taiwan-Diabetes Dataset: https://dep.mohw.gov.tw/DOS/cp-2519-3480-113.html

The "DATA" folder contains the downloaded raw data set and the pre-processed normalised data.

Run

All the hyper-parameters and steps are included in the ./EXPERIMENTS/UAA-GAIN/main.py file, you can run it directly.

All other baseline methods are also in the "EXPERIMENTS" folder.

Complete experimental results

Due to space limitations in the paper, it is not possible to show the results of the experiments for all years. Here we show all the results of the experiments in graphs：

• UK-Obesity results：

• US-Hypertension results：

• Taiwan-Diabetes results：

Citation

@article{feng2022towards,
  title={Towards Sustainable Compressive Population Health: A GAN-based Year-By-Year Imputation Method},
  author={Feng, Yujie and Wang, Jiangtao and Wang, Yasha and Chu, Xu},
  journal={ACM Transactions on Computing for Healthcare},
  year={2022},
  publisher={ACM New York, NY}
}