Preventive Health Care System for Early Heart Disease Detection Using IoT and Machine Learning

The foremost objective of the preventive Health care system is to provide personalized diagnostic care to patients to monitor their medical conditions regularly thereby preventing the disease from developing further. Continuous monitoring of the patient's condition is carried out effectively by using the technology of Sensor/IoT Devices. IoT devices for health care come under the emerging technology Internet of Medical Things (IoMT). The IoMT usage in health care is increasing steadily as it efficiently provides early diagnosis of any health condition. In addition to diagnosis, these devices can also be used to communicate the condition of the patient with other people or health care experts for any suggestion/immediate attention. To provide a better diagnosis, the produced data from these devices can be used to provide a better analysis. Machine Learning algorithms have proved to be efficient in analyzing large volumes of data. A Preventive health care framework is proposed in this paper to predict early heart disease by procuring data such as Heart Rate (HT), Systolic Blood pressure (SBP), and Diastolic blood pressure (DBP) from wearable IoT Devices. The same data is analyzed using Machine Learning algorithms for any anomaly detection. Once any anomaly is detected, the user of that device or the health care practitioner will be alerted by sending an alert message. to provide immediate attention or diagnosis.