An efficient cardiovascular disease prediction model through AI-driven IoT technology.

Conditions affecting the circulatory system and blood vessels are referred to as cardiovascular diseases that include strokes and heart attacks. Internet of Things (IoT) technologies monitor health metrics, identify irregularities and enable remote patient care, resulting in earlier intervention and more individualized therapy. This research aims to establish an efficient cardiovascular disease prediction model through Artificial intelligence (AI)-driven IoT technology. We propose a novel Shuffled Frog leaping-tuned Iterative Improved Adaptive Boosting (SF-IIAdaboost) algorithm for predicting cardiovascular disease with the implementation of IoT device data. IoT medical sensors and wearable devices will collect the patient's clinical data in our proposed framework. Z-score normalization is used to preprocess the gathered data and optimize its quality. Kernel principal component analysis (Kernel-PCA) extracts the relevant features from the processed data. We obtained a dataset that contains various health data gathered from numerous sensing devices to train our recommended model. Our proposed methodology is implemented using Python software. During the evaluation phase, we assess the effectiveness of our model across different parameters. We conduct comparative analyses against conventional methods to ascertain the superiority of our approach. Experimental findings demonstrate the superior performance of our recognition method over traditional approaches. The proposed SF-IIAdaboost algorithm, integrated with IoT device data, presents a promising avenue for predicting cardiovascular disease. The SF-IIAdaboost model demonstrated notable enhancements, attaining 95.37 % accuracy, 93.51 % precision, 94.3 % sensitivity, 96.31 % specificity, and 95.72 % F-measure. Future developments are predicted to involve computing on the edge, where immediate evaluations can be performed in the edge layer to avoid the basic constraints of the clouds, such as high latency, utilization of bandwidth and performing the growth of IoT data. Edge computing can revolutionize the healthcare industry's efficacy by enabling providers to make flexible decisions, operate quickly, and accurately anticipate diseases. It can improve the average level of service standards.