Hidden Markov model for analyzing time-series health checkup data.
In this paper, we apply a Hidden Markov Model (HMM) to analyze time-series personal health checkup data. HMM is widely used for data having continuation and extensibility such as time-series health checkup data. Therefore, using HMM as probabilistic model to model the health checkup data is considered to be suitable, and HMM can express the process of health condition changes of a person. In this paper, a HMM with six states placed in a 2×3 matrix was prepared. We collected training features including the time-series health checkup data. Each feature consists of eight inspection parameters such as BMI, SBP, and TG. The HMM was then built using the training features. In the experiments, we built five HMMs for different gender and age conditions (e.g. male 50's) using thousands of training feature vectors, respectively. Investigating the HMMs we found that the HMMs can model three health risk levels. The models can also represent health transitions or changes, indicating the possibility of estimating the risk of lifestyle-related diseases.
Graduate School of Engineering, Gifu University, Japan.
Ryouhei
R
Kawamoto
Alwis
A
Nazir
Atsuyuki
A
Kameyama
Takashi
T
Ichinomiya
Keiko
K
Yamamoto
Satoshi
S
Tamura
Mayumi
M
Yamamoto
Satoru
S
Hayamizu
Yasutomi
Y
Kinosada
D000328:Adult; D001185:Artificial Intelligence; D003198:Computer Simulation; D003628:Database Management Systems; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003955:Diagnostic Tests, Routine; D005260:Female; D055991:Health Records, Personal; D006801:Humans; D007564:Japan; D008297:Male; D008390:Markov Chains; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D010808:Physical Examination