New Method to Implement and Analysis of Medical System in Real Time

Area of Science:

• Biomedical Engineering
• Health Informatics
• Artificial Intelligence in Medicine

Background:

• Healthcare faces challenges in disease diagnosis and prediction due to human error and measurement inaccuracies.
• Technological advancements in medical devices have transformed healthcare delivery.
• Existing systems may lack comprehensive vital parameter analysis and integration.

Purpose of the Study:

• To develop an integrated health monitoring system for measuring vital parameters and predicting diseases.
• To analyze the impact of emotions on vital signs using biometric data.
• To create a user-friendly interface for clinicians and smartphone users via the Internet of Things.

Main Methods:

• Determination of blood gas percentages through breathing analysis.
• Measurement of vital parameters and analysis of their correlation with emotional states.
• Implementation of a supervised learning model for disease prediction based on biometric data.
• Validation of results against the Omron device, a recognized reference standard.

Main Results:

• The proposed system demonstrated high measurement accuracy, reaching 98.8%.
• The system successfully expanded the database of vital parameters and provided emotional impact analysis.
• The diagnostic accuracy for COVID-19 reached 64% using the developed model.
• A user interface was developed for both clinical and mobile applications.

Conclusions:

• The integrated health monitoring system effectively addresses limitations in current disease diagnosis and prediction.
• The system enhances the accuracy of vital parameter measurement and offers novel insights into emotional influences.
• The developed Internet of Things-based platform facilitates broader accessibility for healthcare monitoring.