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The prediction of disease can facilitate early intervention, comprehensive diagnosis and treatment, thereby benefiting healthcare and reducing medical costs. While single class and multi-class learning methods have been applied for disease prediction, they are inadequate in distinguishing between primary and secondary diagnoses, which is crucial for treatments. In this paper, label distribution is suggested to describe the diagnosis, which assigns the description degree to quantify the diagnosis. Additionally, a novel hierarchical label distribution learning (HLDL) model is proposed to make fine-grained predictions based on the hierarchical classification of diseases, taking into account the relationship among diseases. The experimental results on real-world datasets demonstrate that the HLDL model outperforms the baselines with statistical significance. [ABSTRACT FROM AUTHOR]

A comprehensive scheme of patient similarity based on different types of patient features and the corresponding similarity measurements was proposed. Patient similarity was used in building a predictive model where training samples similar to the index patient were selected instead of randomly selected samples. The predictive models using the proposed patient similarity measurement outperformed those using Euclidean distance based similarity and those not using patient similarity. [ABSTRACT FROM AUTHOR]