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Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study

Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study

DAD: Discharge Abstract Database; PCA: principal component analysis. Similar to the study by Baruah [8], during clinical and geographical preprocessing, individuals were screened for specific criteria.

Ethan Rajkumar, Kevin Nguyen, Sandra Radic, Jubelle Paa, Qiyang Geng

JMIR Form Res 2023;7:e41725

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Separating Features From Functionality in Vaccination Apps: Computational Analysis

Separating Features From Functionality in Vaccination Apps: Computational Analysis

First, we used principal component analysis (PCA) to reduce the feature space from our original data set. Second, the apps were clustered using the k-means algorithm in R (R Foundation for Statistical Computing). The following sections will discuss in detail how PCA and k-means clustering were used in this study. PCA is an important preprocessing step. Prior studies have used PCA to show children’s interactions with education apps [31] and reduce the context dimensions of data from smartphone apps [32].

George Shaw Jr, Devaki Nadkarni, Eric Phann, Rachel Sielaty, Madeleine Ledenyi, Razaan Abnowf, Qian Xu, Paul Arredondo, Shi Chen

JMIR Form Res 2022;6(10):e36818

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