Diabetes Disease Risk Prediction Based on Machine Learning
Dec. 2017 – Mar. 2018
Tag: Machine learning, Health Monitoring,Xgboost, Python
Chronic diseases such as cardiovascular diseases and diabetes account for 80% of the total deaths annually, and the annual expenditure on chronic diseases accounts for more than 13% of China’s public health expenditure. As a common chronic disease, diabetes mellitus can not be cured at present, but it’s incidence can be reduced through scientific and effective prevention and treatment.
In this project, I
- Designed and applied Xgboost to predict diabetes disease risk with features including albumin, cholesterol, etc. Optimized the GBDT to reach higher precision(f1score 0.7).
- Participated into the overall diabetes disease risk prototype system implementation in Python.
