This paper highlights the role of evolving spiking neural networks (an enhanced version of SNN) for predicting medical diagnosis. This article aims to focus on regression problems under a supervised learning strategy. In this paper, we have trained and tested eSNN on benchmarking datasets. Among the three datasets, one is the ICU Dataset which helps in predicting the recovery ration of patients who stayed in ICU. Another dataset is PlasmaRetinol which predicts the risk of cancer-related to certain carotenoids. Dataset pharynx is a part of a study conducted in the USA to determine the success rate of two radiation types. The selected datasets are those which were previously used for BioMedical Engineering related tasks. Later the evaluation was conducted using Regression Metrics. From experiment results, it is concluded that eSNN with standard parameters without optimization performed well but there is still space available for improvement to achieve the highest possible prediction scores. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.