Predicting Rehabilitation Potential with the K-Nearest Neighbors Algorithm: A Comparison with the Current Clinical Assessment Protocol

M. Zhu, W. Chen, J. Hirdes, and P. Stolee (Canada)


Bayes’ theorem; Binary prediction; Diagnostic likelihood ratio; interRAI minimum data set; Machine learning; Mod elling and simulation methodologies.


Using data from eight Community Care Access Centres (CCACs) in Ontario, we demonstrate that an automatic, data-driven, machine learning algorithm such as the K nearest neighbors (KNN) algorithm can predict rehabil itation potential more effectively than the current Clini cal Assessment Protocol (CAP). Implications for clinical decision-making and computerized health information sys tems are discussed.

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