Recent developments in Information and Communication Technologies (ICT) and online healthcare services have created a huge volume of health data. With the advancements in machine learning approaches, the research on Disease Prediction Support System (DPSS) has attracted many researchers globally. In this article, we present a hybrid reasoning-based methodology on predicting diseases. The combinatorial advantage of Fuzzy sety theory, k-nearest neighbor and case-based reasoning helps to yield enhanced prediction results. Though DPSS facilitates promising healthcare services, data security and privacy are still crucial challenging issues to be addressed. The DPSS is extended as a Privacy-Aware Disease Prediction Support System (PDPSS) using Paillier Homomorphic Encryption to preserve patients’ sensitive information from unauthorized user access. The proposed prediction model is evaluated with the statistical evaluation metrics, and the experimental results reveal the improved performance of PDPSS in enhanced prediction accuracy and better security. © 2018 Elsevier Ltd