Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes

IEEE Access

Md. Shafiqul Islam¹  Samir Brahim Belhaouari¹  Marwa K. Qaraqe¹  Muhammad A. Abdul-Ghani² 

[1] College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar;UT Health San Antonio, San Antonio, TX, USA;

关键词: Feature extraction;    fractional derivative;    wavelet transform;    machine learning;    diabetes prediction;   

DOI  :  10.1109/ACCESS.2020.3005540

来源: DOAJ

【 摘 要 】

Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevation of glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to the damage of different body organs and develop life-threatening health complications. Studies show that the progression of diabetes can be stopped or delayed, provided a person follows a healthy lifestyle and takes proper medication. Prevention of diabetes or the delayed onset of diabetes is crucial, and it can be achieved if there exists a screening process that identifies individuals who are at risk of developing diabetes in the future. Although machine learning techniques have been applied for disease diagnosis, there is little work done on long term prediction of disease, type 2 diabetes in particular. Moreover, finding discriminative features or risk-factors responsible for the future development of diabetes plays a significant role. In this study, we propose two novel feature extraction approaches for finding the best risk-factors, followed by applying a machine learning pipeline for the long term prediction of type 2 diabetes. The proposed methods have been evaluated using data from a longitudinal clinical study, known as the San Antonio Heart Study. Our proposed model managed to achieve 95.94% accuracy in predicting whether a person will develop type 2 diabetes within the next 7-8 years or not.

【 授权许可】

Unknown