To Predict Heart Disease Risk and Medications Using Data Mining Techniques With an IoT Based Monitoring System For Post Operative Heart Disease Patients.
Abstract
the most lethal one. This disease attacks a person so instantly
that it hardly gets any time to get treated with. So diagnosing
patients correctly on timely basis is the most challenging task
for the medical fraternity. A wrong diagnosis by the hospital
leads to earn a bad name and loosing reputation.The purpose
of this work is to develop a cost effective treatment using data
mining technologies for facilitating data base decision support
system.The diagnosis of heart disease using different features or
symptoms is a complex activity. In this work two data mining
classification techniques like Artificial Neural Network(ANN) and
Naive Bayes are used to assist in the diagnosis of the heart disease
and medication is provided accordingly. It is very important
to monitor various medical parameters and post operational
days. Hence the latest trend in Health care communication
method using IoT is adapted. In this work the AVR-328 microcontroller(Arduino board) is used as a gateway to communicate to
the various sensors such as temperature sensor, heartbeat sensor,
ECG sensor, sensor for keeping a track of drip levels(blood or
saline) and a sensor to keep track of motion. The micro-controller
picks up the sensor data and sends it to the network through WiFi and hence provides real time monitoring of the health care
parameters for doctors. The data can be accessed anytime by
the doctor. The controller is also connected with buzzer to alert
the caretaker about variation in sensor output. At the time of
extremity situation alert message is sent to the doctor through
the android app connected to the cloud server. Hence quick
provisional medication can be easily done by the doctor by using
NFC tags without manually searching for history of patient. This
system is efficient with low power consumption capability, easy
setup, high performance and time to time response.
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