<p>Cloud computing technology has enabled easy and anytime access to healthcare services. This is more apparent in the use of cloud computing for storing and processing data generated from wearable medical devices. Daily wearable medical devices generate a large amount of data. This large amount of data is also known as “big data.” Data analytics of the big data generated by medical devices enables the early detection of diseases while facilitating the prompt provision of the required medical care, particularly in the case of patients affected by critical or chronic health debilities. In this context, the study analyzes the challenges in big data security and privacy for accurate diagnosis and treatment of diseases by medical devices, and also discusses possible solutions for healthcare service providers to make the data secure. The authors have designed a Machine Learning (ML)-based framework for selecting the best security algorithm based on their security parameters. The authors classify the algorithms and find that the triple encryption technique is more secure than other hybrid security algorithms. This study uses a Support Vector Machine (SVM)-based classifier for selecting the best security algorithm and compares the selection results with the Decision Tree classifier. Furthermore, the study also focuses on the rapidly emerging security technologies in healthcare that could facilitate better healthcare services and future prospects in this context.</p>