Lakhasly

The challenges in the design of a smart healthcare system, such as increasing system complexity, which lead to higher energy consumption and design costs are discussed in this paper.Based on the use of the model proposed in this study, an IoT-based CE device can be imple mented in the future to predict various pathological conditions of diseases, thereby improving the healthcare systemWe used our model to predict the pathological conditions of cardiovascu lar diseases and compared our results with those obtained using various other mod els.In addition, a reduction in the execution time in comparison with other methods, such as DT, NB, K-NN, and SVM, can be achieved.In this study, we propose the FPGA implementation of a fuzzy clas sifier for IoT-based healthcare systems.To overcome these drawbacks, various new computing methods are being designed.These systems can be used to predict patho logical conditions with improved accuracy.The results reveal that the proposed model yields the best outcome.

The challenges in the design of a smart healthcare system, such as increasing system
complexity, which lead to higher energy consumption and design costs are discussed
in this paper. To overcome these drawbacks, various new computing methods are
being designed. In this study, we propose the FPGA implementation of a fuzzy clas
sifier for IoT-based healthcare systems. These systems can be used to predict patho
logical conditions with improved accuracy. In addition, a reduction in the execution
time in comparison with other methods, such as DT, NB, K-NN, and SVM, can be
achieved. We used our model to predict the pathological conditions of cardiovascu
lar diseases and compared our results with those obtained using various other mod
els. The results reveal that the proposed model yields the best outcome. Based on
the use of the model proposed in this study, an IoT-based CE device can be imple
mented in the future to predict various pathological conditions of diseases, thereby improving the healthcare system