Open Access

ARTICLE

Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

by Ashir Javeed^1,2, Ana Luiza Dallora², Johan Sanmartin Berglund^2,*, Arif Ali⁴, Peter Anderberg^2,3, Liaqat Ali⁵

1 Aging Research Center, Karolinska Institutet, Stockholm, 17165, Sweden
2 Department of Health, Blekinge Institute of Technology, Karlskrona, 37141, Sweden
3 School of Health Sciences, University of Skövde, Skövde, SE 541 28, Sweden
4 Department of Computer Science, University of Science and Technology Bannu, Bannu, 28100, Pakistan
5 Department of Electrical Engineering, University of Science and Technology Bannu, Bannu, 28100, Pakistan

* Corresponding Author: Johan Sanmartin Berglund. Email:

Computers, Materials & Continua 2023, 75(2), 2491-2508. https://doi.org/10.32604/cmc.2023.033783

Received 27 June 2022; Accepted 09 September 2022; Issue published 31 March 2023

Abstract

Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.

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Keywords

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