A combination of system dynamics and machine learning: Explaining and predicting the progression of patients developing dementia
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2023-07-11
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en
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Dementia is a progressive and complex disease and one of the largest healthcare problems of this century worldwide. This study examined the integrated performance of qualitative system dynamics and quantitative machine learning for explaining and predicting cognitive decline in dementia to support medical decision-making. Rush Alzheimer's Disease Center provided the data used in this study, and specific variables were selected based on a system dynamics model covering variables contributing to cognitive decline in dementia patients—these variables were then analysed with descriptive analyses followed by unsupervised and supervised machine learning. Unsupervised machine learning was done with the k-Means clustering algorithm for uncovering hidden patterns, and supervised machine learning was done with regression decision trees and the random forest algorithm for predicting. The findings of this study revealed that the combination of system dynamics and machine learning strengthen each other, mitigates the limitations of each approach, and provides a more thorough understanding of the factors that contribute to cognitive decline in dementia patients. The proposed iterative integration process, with the involvement of experts, could further increase the performance of this integration, leading to collective insights that can support medical decision-making.
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Faculteit der Managementwetenschappen