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A new paradigm for supporting medical services, especially beneficial for metropolitan regions and individuals experiencing homelessness who use technological communications, is fog-based healthcare service management integrated with the Internet of Things (IoT). This paradigm allows for the flexible transformation of health data into personalized, meaningful health knowledge, potentially having a significant impact on health practices in communities where health departments are not actively engaged. Fog computing and the IoT are crucial components of today's healthcare system, facilitating the management of vast amounts of big data for disease prediction and diagnosis. However, there is a risk of incorrect diagnosis when a patient has multiple illnesses. This paper aims to develop a model for the diagnosis of cardiovascular diseases and diabetes using a combination of AI and IoT approaches. The proposed model encompasses data collection, preprocessing, classification, and parameter setting. Wearables and sensors, which are part of the IoT, facilitate easy data collection, while artificial intelligence methods use this data for disease detection. As an example of intelligent healthcare systems, the proposed approach employs the Smart Healthcare-Crow Search Optimization (SH-CSO) algorithm to diagnose diseases. By adjusting the "weight" and "bias" parameters of the intelligent healthcare systems model, CSO enhances the classification of medical data. The application of CSO significantly improves the diagnostic outcomes of the intelligent healthcare systems model. The efficacy of the SH-CSO algorithm was validated using medical records. Results demonstrated that the proposed SH-CSO model could diagnose diabetes with a maximum accuracy of 97.26% and heart disease with a maximum accuracy of 96.16%.
Tang et al. (Mon,) studied this question.
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