Volume 8 , Issue 1 , PP: 40-50, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud A. Zaher 1 , Nabil M. Eldakhly 2 , Yahia B. Hassan 3
Doi: https://doi.org/10.54216/IJWAC.080105
The study gives a complete plan for lowering disease through the use of ICT in personal healthcare. The Health Pattern Recognition (HPR), Dynamic Risk Assessment (DRA), and Personalized Intervention Strategy (PIS) formulas are all parts of this method. They are used to collect, prepare, and use data. This research focuses on cybersecurity using health pattern recognition (HPR), dynamic risk assessment (DRA), and personalized intervention strategies (PIS). PIS offers a comprehensive disease prevention approach in personal healthcare that takes advantage of technological advancements. Because they integrate secure data processing with privacy-preserving machine learning, these aspects assure the safety and validity of health data collected from wearable devices. This option allows for the assessment of medical records. It may be helpful to analyze the technique's accuracy and adherence to established security standards in order to evaluate its application for disease prediction and preventive health management. The HPR program looks at each person's health information to find trends in diseases and other results using machine learning. This helps with early evaluation and healthcare management that avoids problems. DRA keeps a person's risk rating up to date so that it takes into account any changes in their health. After that, people are given choices based on the results and risks that PIS has predicted. Some of the tests that were used to compare the suggested method to industry standards were accuracy, sensitivity, specificity, precision, and the Matthews Correlation Coefficient. The suggested way seems to work because it has better predicting power, fewer fake positives, and more users who are involved in preventive health management.
Accuracy, Algorithm , Data Collection , Dynamic Risk Assessment , Feature Extraction , Health Pattern Recognition , Intervention, Machine Learning , Metrics , Personalized , Preprocessing , Proactive , Sensitivity , Specificity , Wearable Devices.
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