Multimorbidity Prediction Using Data Mining Model
Authors: Najah Al-shanableh, Mofleh Al Diabat
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This research aims to use data mining to predict health care outcomes. We will investigate patterns of multiple chronic conditions (MCCs), or multimorbidity, among the US elderly population. The multimorbidity prediction model, as a general aspect, was not found in the literature, although some researchers have been exploring the risk of developing further chronic conditions after reporting an index disease. Data mining can provide richer results compared to those produced using a statistical approach and greater depth and breadth. It can also help professionals to identify the best time to intervene. In this research, the primary focus was on building disease knowledge using data mining algorithms for MCCs in the elderly. We identified potential morbidity groups using clustering and tested several prediction models on HCUP real data with high accuracy, where the highest accuracy of 99.05% was achieved by Logistic Regression.