A Clustering-Aided Approach for Diagnosis Prediction: A Case Study of Elderly Fall

Abstract

Data-driven diagnosis prediction has been adopted in clinical decision support systems. However, only a few studies have focused on non-supervised clustering approaches to building a high-quality patient data set. This study focused on a clustering-aided approach to diagnosis prediction. We leveraged clustering-aided machine learning models to predict elderly falls. First, we used patients' risk factors to build a feature set. The feature set showed a clustering-aided approach could aggregate patient factors that shared similar clinical and demographic characteristics. Subsequently, a K-means clustering approach significantly improved the data set quality. Overall, our study demonstrated that clustering approaches improve the prediction performance of elderly falls. A clustering-aided approach can be applied to similar clinical healthcare practices to potentially improve elderly care.