Code repository of the paper "Gradient Boosting Decision Tree Becomes More Reliable Than Logistic Regression in Predicting Probability for Diabetes With Big Data" (Seto et al. 2022)
One of the GBDT-based diabetes prediction models for evaluating the model performance used in the paper is available.
The code can be run in Python. The code require the installation of the following packages:
- Pickle
- Pandas
- LightGBM (https://lightgbm.readthedocs.io/en/latest/)
Put the CSV file you want to predict in the DATA folder (test.csv is already included).
- Data Description
| Name | Details |
|---|---|
| age | |
| sex | 1: Woman, 0: Man |
| bmi | |
| shuushuku_ketsuatsu | Systolic blood pressure |
| chuusei_shibou | Triglyceride cholosterol |
| hdl_cholesterol | |
| ldl_cholesterol | |
| gpt | Alanine aminotransferase |
| hba1c | Glycated hemoglobin A1c |
| nyoutampaku | Urinary protein, 1: positive, 0: negative |
| kitsuen | Smoking |
| fukuyaku_ketsuatsu | Anti hypertension drug |
| fukuyaku_shishitsu | Anti dyslipidemia drug |
| kioureki_noukekkan | Medical history of Stroke |
| kioureki_shinkekkan | Medical history of Heart disease |
| kioureki_jinfuzen_jinkoutouseki | Medical HIstory of Renal failure |
$ python prediction.py