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A decision tree multifactorial approach for predicting dementia in a 10 years’ time
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

Background: Dementia is a complex neurological disorder, to which little is known about its mechanisms and no therapeutic treatment was identified, to date, to revert or alleviate its symptoms. It affects the older adults population causing a progressive cognitive decline that can become severe enough to impair the individuals' independence and functioning. In this scenario, the prognosis research, directed to identify modifiable risk factors in order to delay or prevent its development, in a big enough time frame is substantially important.

Objective: This study investigates a decision tree multifactorial approach for the prognosis of dementia of individuals, not diagnosed with this disorder at baseline, and their development (or not) of dementia in a time frame of 10 years. 

Methods: This study retrieved data from the Swedish National Study on Aging and Care, which consisted of 726 subjects (313 males and 413 females), of which 91 presented a diagnosis of dementia at the 10-year study mark. A K-nearest neighbors multiple imputation method was employed to handle the missing data. A wrapper feature selection was employed to select the best features in a set of 75 variables, which considered factors related to demographic, social, lifestyle, medical history, biochemical test, physical examination, psychological assessment and diverse health instruments relevant to dementia evaluation. Lastly, a cost-sensitive decision tree approach was employed in order to build predictive models in an stratified nested cross-validation experimental setup.

Results: The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Our findings showed that most of the variables selected by the tree are related to modifiable risk factors, of which physical strength was an important factor across all ages of the sample. Also, there was a lack of variables related to the health instruments routinely used for the dementia diagnosis that might not be sensitive enough to predict dementia in a 10 years’ time.

Conclusions: The proposed model identified diverse modifiable factors, in a 10 years’ time from diagnosis, that could be investigated for possible interventions in order to delay or prevent the dementia onset. 

National Category
Other Medical Sciences not elsewhere specified
Identifiers
URN: urn:nbn:se:bth-19499OAI: oai:DiVA.org:bth-19499DiVA, id: diva2:1432307
Note

This manuscript has not yet been submitted.

Available from: 2020-05-26 Created: 2020-05-26 Last updated: 2020-06-02Bibliographically approved
In thesis
1. Machine learning applications in healthcare
Open this publication in new window or tab >>Machine learning applications in healthcare
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare.

The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts.

In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment.

In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 202
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 6
Keywords
Machine learning, Healthcare, Diagnosis, Prognosis, Age assessment, Bone age assessment, Dementia, Applied health technology
National Category
Other Medical Sciences
Research subject
Applied Health Technology
Identifiers
urn:nbn:se:bth-19513 (URN)978-91-7295-405-2 (ISBN)
Public defence
2020-09-16, 14:00
Opponent
Supervisors
Available from: 2020-05-29 Created: 2020-05-28 Last updated: 2021-01-27Bibliographically approved

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Dallora Moraes, Ana LuizaMendes, EmiliaAnderberg, PeterBerglund, Johan Sanmartin
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