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An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes
Author(s) -
Shetaban Samar,
Seyyed Esfahani Mir Mehdi,
Saghaei Abbas,
Ahmadi Abbas
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12418
Subject(s) - disease , medicine , diabetes mellitus , estimation , markov model , type 2 diabetes , markov chain , computer science , intensive care medicine , risk analysis (engineering) , machine learning , engineering , endocrinology , systems engineering
Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.