Hidden Markov model for analyzing time-series health checkup data

Area of Science:

• Computational Biology
• Health Informatics
• Statistical Modeling

Background:

• Time-series personal health checkup data requires robust analytical methods.
• Hidden Markov Models (HMMs) are suitable for analyzing sequential and continuous data like health records.
• HMMs can effectively model the dynamic process of an individual's health condition changes.

Purpose of the Study:

• To apply a Hidden Markov Model (HMM) for analyzing time-series personal health checkup data.
• To develop a probabilistic model capable of representing health condition changes and risk levels.
• To explore the potential of HMMs in estimating the risk of lifestyle-related diseases.

Main Methods:

• A Hidden Markov Model (HMM) with a 2x3 matrix of six states was designed.
• Training data comprised time-series health checkup records with eight inspection parameters (e.g., BMI, SBP, TG).
• Five distinct HMMs were constructed, tailored for specific gender and age demographics (e.g., males in their 50s).

Main Results:

• The developed HMMs demonstrated the ability to model three distinct health risk levels.
• The models successfully represented health transitions and changes over time.
• Findings indicate the potential for HMMs to predict the risk associated with lifestyle-related diseases.

Conclusions:

• Hidden Markov Models provide a suitable framework for analyzing complex personal health checkup data.
• HMMs can effectively categorize health status into risk levels and track dynamic health changes.
• This approach offers a promising method for early risk estimation of lifestyle-related diseases.