Handling Irregularly Sampled Longitudinal Data and Prognostic Modeling of Diabetes Using Machine Learning Technique

Handling Irregularly Sampled Longitudinal Data and Prognostic Modeling of Diabetes Using Machine Learning Technique

Clinical researchers use prognostic modeling techniques to identify a-prior patient health status and characterize progression patterns. It is highly desirable to predict future health condition especially to implement preventive and intervention strategies in pre-diabetic individuals. Hidden Markov...

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Bibliographic Details
Main Authors: Sajida Perveen, Muhammad Shahbaz, Tanzila Saba, Karim Keshavjee, Amjad Rehman, Aziz Guergachi
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Type 2 diabetes mellitus (T2DM)
risk prediction
Newton‘s divided difference method (NDDM)
irregular and sparsely sampled data handling
approximation technique
HMM
Online Access:https://ieeexplore.ieee.org/document/8966591/

Internet

https://ieeexplore.ieee.org/document/8966591/

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