Most of the hospitals in Malaysia still utilise manual inspection by medical
personnel to determine the health conditions of the patients. The data
collected from the medical equipment would have to be analysed and verified
by the hospital. Frequently, many patients need medical inspections.
However, to provide a precise diagnosis, medical personnel requires more
time. This limitation can be addressed by the development of automated and
wireless health monitoring systems with health diagnostic feature supported
by artificial intelligence (AI). In this project, the objective is to develop a
prototype of a wireless (non-invasive) heartbeat monitoring system with
supervised learning. This system monitors the heartbeat activity and predicts
the condition of the user's heartbeat. Technically, a photoplethysmographybased (PPG-based) heartbeat sensor is used to build a heartbeat sensing
device with a Bluetooth feature that communicates with an Android
application. The Android application is developed to receive heartbeat data
from the device and feed the data into an AI classification model to predict
the heartbeat condition of the user. This AI classifier was built from
heartbeat data collected from 10 healthy people. The additional heartbeat
dataset was generated based on a sound source of heartbeat information to
increase the volume of the training dataset. The completion of this project
implementation results in a wireless heartbeat monitoring system that can be
applied regardless of location and time. The accuracy of the AI prediction is
99 % when evaluated with a testing dataset. The empirical accuracy obtained
by testing the system with actual implementation is 90 %.