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

Agostino Marengo1, Alessandro Pagano2, Vito Santamato3

  • 1Department of Agricultural Sciences, Food, Natural Resources, and Engineering University of Foggia, Foggia, Italy.

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Summary

This study introduces a new AI-driven Internet of Things (IoT) method for predicting cardiovascular disease, achieving high accuracy. The Shuffled Frog leaping-tuned Iterative Improved Adaptive Boosting (SF-IIAdaboost) algorithm offers a promising approach for early detection and personalized treatment.

Keywords:
AI-Driven IoTCardiovascular disease predictionHealth careShuffled frog leaping-tuned iterative improved adaptive boosting (SF-IIAdaboost)

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Area of Science:

  • Cardiovascular health
  • Artificial Intelligence in Medicine
  • Internet of Things (IoT) applications

Background:

  • Cardiovascular diseases (CVDs) like strokes and heart attacks pose significant health risks.
  • Traditional methods for CVD detection can be limited in early intervention and personalization.
  • IoT technologies offer continuous health monitoring and remote patient care capabilities.

Purpose of the Study:

  • To develop an efficient cardiovascular disease prediction model using Artificial Intelligence (AI)-driven IoT technology.
  • To introduce a novel Shuffled Frog leaping-tuned Iterative Improved Adaptive Boosting (SF-IIAdaboost) algorithm for CVD prediction.
  • To leverage real-time clinical data from IoT devices for enhanced diagnostic accuracy.

Main Methods:

  • Collected patient clinical data using IoT medical sensors and wearable devices.
  • Preprocessed data with Z-score normalization and extracted features using Kernel Principal Component Analysis (Kernel-PCA).
  • Implemented and evaluated the proposed SF-IIAdaboost algorithm using Python, comparing its performance against conventional methods.

Main Results:

  • The SF-IIAdaboost model achieved high performance metrics: 95.37% accuracy, 93.51% precision, 94.3% sensitivity, 96.31% specificity, and 95.72% F-measure.
  • Demonstrated superior performance compared to traditional cardiovascular disease prediction approaches.
  • Validated the effectiveness of AI-driven IoT data integration for CVD prediction.

Conclusions:

  • The proposed SF-IIAdaboost algorithm integrated with IoT data presents a significant advancement in cardiovascular disease prediction.
  • This AI-driven approach enables earlier intervention and more individualized patient therapy.
  • Future work will focus on edge computing for real-time analysis, further enhancing healthcare efficacy and disease anticipation.