Authors:
Christina Brester
1
;
Vladimir Stanovov
2
;
Ari Voutilainen
3
;
Tomi-Pekka Tuomainen
3
;
Eugene Semenkin
2
and
Mikko Kolehmainen
4
Affiliations:
1
Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland, Institute of Computer Science and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk and Russia
;
2
Institute of Computer Science and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk and Russia
;
3
Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio and Finland
;
4
Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio and Finland
Keyword(s):
Predictive Modeling, Fuzzy Logic, Genetic Algorithm, Kuopio Ischemic Heart Disease Risk Factor Study, Heterogeneity, Performance Improvement, Knowledge Discovery.
Abstract:
Various data-driven models are often involved in epidemiological studies, wherein the availability of data is constantly increasing. Accurate and, at the same time, interpretable models are preferable from the practical point of view. Finding simple and compact dependences between predictors and outcome variables makes it easier to understand necessary interventions and preventive measures. In this study, we applied a Fuzzy Logic-based model, which meets these requirements, to predict the coronary heart disease (CHD) progression during a 30-year follow-up. The Fuzzy Logic-based model was automatically designed with an ad hoc Genetic Algorithm using the data from the Kuopio Ischemic Heart Disease Risk Factor (KIHD) Study, a Finnish cohort of 2682 men who were middle-aged at baseline in 1980s. Using cross-validation, we found out that the sample from the KIHD study is heterogeneous and after filtering out 10% of outliers, the predictive accuracy increased from 65% to 73%. The generated
rule bases include 19 fuzzy rules on average with maximum 7 variables in one rule from the initial set of 638 predictor variables. The selected predictors of CHD progression are informative and diverse representing physical aspects, behavior, and socioeconomics. The Fuzzy Logic-based model creates a comprehensive set of predictors that enables us to better understand the complexity of illnesses and their progression. Moreover, the Fuzzy Logic-based model has potential to provide tools to analyse and deal with heterogeneity in large cohorts.
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