Intelligent health monitoring technologies are being developed increasingly due to the limitations of traditional healthcare systems, which often rely on periodic measurements and fail to detect early physiological issues in real time. A significant challenge is accurately analyzing anomalies in continuous biometric data, as these measurements are generally unstable. Traditional statistical methods, such as the Z-score and threshold-based approaches, are quick and computationally simple but can result in a high number of false alarms. In contrast, unsupervised intelligent machine learning methods provide a deeper understanding of data patterns and do not depend on linear or batch learning techniques, which can be time-consuming and require substantial data storage.

In this paper, we propose a hybrid health monitoring system based on the Internet of Things (IoT). This system includes real-time data collection using the ESP32 microcontroller, along with cloud storage through Firebase and advanced data analytics via Google Colab. We combine the Z-score statistical method with a dynamic threshold process and the Isolation Forest algorithm for monitoring abnormalities. This approach leverages the strengths of both statistical methods and machine learning to reduce false alarms effectively. We conducted tests that successfully detected various pathological patterns in real-time through a smartphone app implementation. We believe this system is feasible, flexible, and scalable enough to be widely used for patient monitoring, particularly in telehealth, elderly care, and preventive medicine.

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