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Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia
Blekinge Institute of Technology, Faculty of Engineering, Department of Health.ORCID iD: 0000-0003-4190-3532
Blekinge Institute of Technology, Faculty of Engineering, Department of Health.ORCID iD: 0000-0001-9870-8477
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. (SERL)ORCID iD: 0000-0001-9336-4361
King Abdulaziz University, Saudi Arabia.
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2024 (English)In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 11, article id 1336255Article in journal (Refereed) Published
Abstract [en]

Introduction: Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychological testing. However, these methods are time-consuming and costly in terms of treatment. Therefore, this study aims to present a noninvasive method for the early prediction of dementia so that preventive steps should be taken to avoid dementia.

Methods: We developed a hybrid diagnostic system based on statistical and machine learning (ML) methods that used patient electronic health records to predict dementia. The dataset used for this study was obtained from the Swedish National Study on Aging and Care (SNAC), with a sample size of 43040 and 75 features. The newly constructed diagnostic extracts a subset of useful features from the dataset through a statistical method (F-score). For the classification, we developed an ensemble voting classifier based on five different ML models: decision tree (DT), naive Bayes (NB), logistic regression (LR), support vector machines (SVM), and random forest (RF). To address the problem of ML model overfitting, we used a cross-validation approach to evaluate the performance of the proposed diagnostic system. Various assessment measures, such as accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and Matthew’s correlation coefficient (MCC), were used to thoroughly validate the devised diagnostic system’s efficiency.

Results: According to the experimental results, the proposed diagnostic method achieved the best accuracy of 98.25%, as well as sensitivity of 97.44%, specificity of 95.744%, and MCC of 0.7535.

Discussion: The effectiveness of the proposed diagnostic approach is compared to various cutting-edge feature selection techniques and baseline ML models. From experimental results, it is evident that the proposed diagnostic system outperformed the prior feature selection strategies and baseline ML models regarding accuracy.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2024. Vol. 11, article id 1336255
Keywords [en]
dementia, voting classifier, F-score, machine learning, feature selection
National Category
Computer Sciences Geriatrics
Research subject
Software Engineering; Computer Science; Applied Health Technology
Identifiers
URN: urn:nbn:se:bth-25877DOI: 10.3389/fbioe.2023.1336255ISI: 001153187700001Scopus ID: 2-s2.0-85182656352OAI: oai:DiVA.org:bth-25877DiVA, id: diva2:1825239
Projects
National E-Infrastructure for Aging Research (NEAR)Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-09-19Bibliographically approved

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Javeed, AshirAnderberg, PeterGhazi, Ahmad NaumanSanmartin Berglund, Johan

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