Abstract
E-health is a modern technology produced with the evolution and amalgamation of modern technologies such as the Internet of things (IoT) and machine learning (ML). The exploitation of efficient and suitable ML techniques to obtain appropriate data can enhance the mechanism of detection and ultimately prevent diseases. However, the datasets available in repositories for computerized medical analysis are inappropriate, incomplete, and prone to alteration and attacks. In this work, we consider attacks such as poison and evasion and analyze their effect on the decision-making processes in e-health. The results illustrate that the performance of the original model is high in almost all cases compared to the accuracy attained by the combined poisoned model. Interestingly, although the performance of the original model is higher, the difference is not that significant. For example, the artificial neural network achieves an accuracy of 75.39% on the original set. On the poisoned set, the artificial neural network achieves an accuracy of 74.5%. This means that the overall difference is just 1%. A similar trend can be found with the other classifiers except for the SVM and the logistic regression, where the difference is comparatively high. As such, our research proves that the protection of data in the training and testing phase is comparatively more important than the selection and application of the best ML technique.