Prediction, Classification and Recommendation in e-Health via Contextual Partitioning

In this paper, we propose a multipurpose contextual partitioning based estimation algorithm. Exploiting the similarities between contexts (side information: such as age, Gender etc.,) related to patient data in healthcare repository or database, multidimensional spheres are generated over Euclidean space. Then, conditional first and second order characteristics are predicted using sample-based mean and covariance. These conditional statistics of particular patient data subset (sphere) serve the following purposes: i) Prediction for missing values (conditional mean), ii) Partitioned principal components for better classification (conditional covariance) and iii) Recommendation for medical Test or physician (conditional covariance). The proposed approach uniformly partitions the context space into spheres, and then, for each sphere estimates the conditional mean and covariance using only the data (excluding the context data) in the selected sphere. Hence, providing three in one solution i.e., Prediction, Classification and Recommendation for healthcare data using conditional probabilistic characteristics. The overall error is decomposed into estimation and approximation errors. In a particular sphere, estimation error is dependent on the number of instances, while approximation error is dependent on the dissimilarity of instances.