Why the study?
The increasing number of IoT devices in healthcare raises cybersecurity risks due to inadequate network segmentation and lack of standardized protection protocols.
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A proposed Improved Self-Adaptive Bayesian Algorithm (ISABA) for IoT-based healthcare systems improves the diagnostic accuracy of heart disease prediction compared to standard machine learning models.
The main concept of the Internet of Things is to equip the network infrastructure with software and interoperable communication protocols for improved data collecting with an appropriate connection and information sharing between nodes on several heterogeneous networks. IoT applications include smart grid, pollution monitoring, healthcare, and home automation systems for smart lighting. Inappropriately, a large number of devices connected to the Internet increase the likelihood of cybercrime. The Internet of Things device gathers patient cardiac data both before and after a heart issue occurs. When patient health indicators can be remotely tracked in real time, saved, and then communicated to a data center, such as the cloud, the effectiveness, affordability, and accessibility of healthcare systems are all greatly improved. A heart illness detection model (ICA) has been built using a modified version of the Imperialist Competitive Algorithm, a competitive algorithm from the Imperialist series. Due to inadequate network segmentation, devices that are directly exposed to the internet have created a backdoor that allows hackers to enter. To safeguard IoT networks and devices, researchers and marketers are working on a wide range of solutions. Rather, a lot of work needs to be done before the standards are set.
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Nandy et al. (2025) studied Heart disease. Improved Self-Adaptive Bayesian Algorithm (ISABA) vs. Previous algorithms (CNN, Deep learning, ANN) was evaluated on Prediction rate and diagnostic accuracy.
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