A Review on Heart Disease Prediction Using Supervised Learning Techniques

Nowadays Health care System using Internet of Things (IoT) provides better efficiency than the traditional health care systems. Health care using IoT provides the easiest way of communication between patients and doctors. The patient`s health is monitored continuously by the doctor through IoT devices which in turn produces the data pertaining to patient`s health. According to the data received from the IoT devices, the doctors can make a diagnosis of patient health on Real-time. It can be done through Machine Learning (ML) algorithms. This ML technique helps to minimize the disease recurrence by alerting the doctor by identifying the risk factors of a patient`s health. The system uses various supervised learning algorithms such as Logistic Regression, Support Vector Machine, Random Forest, XG Boost Algorithms for disease diagnosis and prediction. The algorithms are then compared using evaluation metrics.