Importance of medical data preprocessing in predictive modeling and risk factor discovery for the frailty syndrome

• Published in: BMC medical informatics and decision making Vol. 19; no. 1; pp. 33 - 17
• Main Authors: Hassler, Andreas Philipp, Menasalvas, Ernestina, García-García, Francisco José, Rodríguez-Mañas, Leocadio, Holzinger, Andreas
• Format: Journal Article
• Language: English
• Published: London BioMed Central 18.02.2019; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accidental falls; Age; Age related diseases; Aging; Algorithms; Analysis; Artificial intelligence; Clinical decision making; Clinical medicine; Data analysis; Data mining; Data preprocessing; Data processing; Decision making; Decision support systems; Frailty; Geriatrics; Health; Health care; Health care information services; Health data analytics; Health Informatics; Information Systems and Communication Service; Life expectancy; Life span; Machine learning; Management of Computing and Information Systems; Mathematical models; Medicine; Medicine & Public Health; modeling; Mortality; Older people; Parameters; Physicians; Prediction models; Predictive modeling; Principal components analysis; Research Article; Risk analysis; Risk factor discovery; Risk factors; Risk factors (Health); Support vector machines; technology; Health data analytics; Data preprocessing; Machine learning; Risk factor discovery; Missing value imputation; Predictive modeling; Data mining; Frailty syndrome
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-019-0747-6

Background Increasing life expectancy results in more elderly people struggling with age related diseases and functional conditions. This poses huge challenges towards establishing new approaches for maintaining health at a higher age. An important aspect for age related deterioration of the general patient condition is frailty. The frailty syndrome is associated with a high risk for falls, hospitalization, disability, and finally increased mortality. Using predictive data mining enables the discovery of potential risk factors and can be used as clinical decision support system, which provides the medical doctor with information on the probable clinical patient outcome. This enables the professional to react promptly and to avert likely adverse events in advance. Methods Medical data of 474 study participants containing 284 health related parameters, including questionnaire answers, blood parameters and vital parameters from the Toledo Study for Healthy Aging (TSHA) was used. Binary classification models were built in order to distinguish between frail and non-frail study subjects. Results Using the available TSHA data and the discovered potential predictors, it was possible to design, develop and evaluate a variety of different predictive models for the frailty syndrome. The best performing model was the support vector machine (SVM, 78.31%). Moreover, a methodology was developed, making it possible to explore and to use incomplete medical data and further identify potential predictors and enable interpretability. Conclusions This work demonstrates that it is feasible to use incomplete, imbalanced medical data for the development of a predictive model for the frailty syndrome. Moreover, potential predictive factors have been discovered, which were clinically approved by the clinicians. Future work will improve prediction accuracy, especially with regard to separating the group of frail patients into frail and pre-frail ones and analyze the differences among them.