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A Context-Aware MRIPPER Algorithm for Heart Disease Prediction.

Saad Almutairi1, S Manimurugan1, Naveen Chilamkurti2

  • 1Industrial Innovation and Robotics Center, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.

Journal of Healthcare Engineering
|July 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an IoT-enabled healthcare system using context awareness for heart disease prediction. The Modified Repeated Incremental Pruning to Produce Error (MRIPPER) algorithm achieved high accuracy, demonstrating its effectiveness in medical data analysis.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Internet of Things (IoT)

Background:

  • Mobile computing and advanced technologies are increasingly integrated into daily life, including the medical domain.
  • Context-aware systems and applications are emerging technologies transforming healthcare delivery.
  • The need for efficient patient data analysis and disease prediction is paramount.

Purpose of the Study:

  • To develop an Internet of Things (IoT)-enabled healthcare system leveraging context awareness for patient data analysis.
  • To utilize the Modified Repeated Incremental Pruning to Produce Error (MRIPPER) algorithm for classifying and predicting heart disease.
  • To evaluate the performance of the proposed system against other machine learning algorithms.

Main Methods:

  • Implementation of an IoT-enabled healthcare system using smart medical devices for data collection and storage.
  • Application of the Modified Repeated Incremental Pruning to Produce Error (MRIPPER) algorithm, a rule-based machine learning technique, for data analysis.
  • Simulation and performance comparison using MATLAB against Random Forest, J48, CART, JRip, and OneR algorithms.

Main Results:

  • The proposed MRIPPER-based model achieved high performance metrics: 98.89% accuracy, 96.76% precision, 99.05% sensitivity, 94.35% specificity, and 97.60% F-score.
  • Accurate predictions were made for both normal (97.38%) and abnormal (97.93%) subjects.
  • The proposed model outperformed other compared machine learning algorithms in heart disease prediction.

Conclusions:

  • The developed IoT-enabled healthcare system effectively utilizes context awareness and the MRIPPER algorithm for accurate heart disease prediction.
  • The MRIPPER algorithm demonstrates superior performance in analyzing medical datasets compared to traditional methods like Random Forest and J48.
  • This approach offers a promising solution for improving diagnostic accuracy and patient outcomes in healthcare through advanced technology integration.