Implementation of IoT-Based Healthcare Kit

Abstract

Cardiovascular diseases and Cardiac Arrhythmia are the most familiar reasons for death throughout the world over the last few decades across the world. However, it is difficult to examine patients in all cases accurately, and consultation with a patient for 24 hours by a doctor is not possible as it needs extra patience, expertise, and time. Thus, with ECG sensors, Arduino, and Raspberry Pi, we implemented machine learning models based on K-Nearest Neighbour, Logistic Regression, Support Vector Machine, and Random Forest for heart disease prediction based on the parameters and attributes related to cardiovascular disease. The datasets in this research are available publicly on the UCI website. The early diagnosis of cardiovascular diseases assists in making decisions on lifestyle changes in patients prone to high risk of heart diseases and minimizing the complications. The result of this research can be a milestone in medicine.