<![CDATA[Monitoring cardiovascular patients at home is often constrained by the interpretation of raw sensor data. This service aimed to implement a portable decision support system to convert raw heart rate (BPM) and oxygen saturation (SpO2) data into meaningful health condition information. The method used was designing a prototype based on NodeMCU ESP8266 and the MAX30100 sensor for data acquisition , and applying Mamdani Fuzzy Logic for analysis. A Fuzzy Inference System (FIS) in MATLAB used 9 rule-bases to classify 2 inputs (BPM and SpO2) into 3 outputs ('Healthy', 'Caution', 'Emergency'). The case study object was a patient with a history of hypertensive heart disease and aortic aneurysm. The results showed the prototype had high accuracy, reaching 95.98% for heart rate and 95.31% for oxygen saturation. The fuzzy logic system successfully classified 35 test datasets, with 34 detected as "Healthy" and 1 as "Caution," proving its sensitivity in monitoring. Oxygen saturation was proven to be the dominant factor for 'Emergency' conditions, while heart rate more influenced the transition from "Healthy" to "Caution". This system provides a practical solution for families to monitor patient conditions more effectively.]]>
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