Smart Health Monitoring System using IOT and Machine Learning Techniques









Abstract

Coronary illness is that the principle purpose for death around the world. Human services field contains a tremendous measure of information, for handling those information certain methods are utilized. Handling or processing is one in all methods regularly utilized. This strategy predicts the emerging potential outcomes of cardiovascular ailment. The results of this strategy are to foresee the previous cardiovascular malady. The task manages IOT using sensor (pulse sensor to watch pulse) with Arduino and furthermore the outcome can be checked in sequential screen. With the use of IFTTT the readings of sensor are perused in google sheet which is then changed over into csv go looking like data. The datasets utilized are grouped as far as therapeutic parameters which are additionally utilized for preparing and testing the information. This strategy assesses those parameters utilizing information preparing order method. With the work of AI calculations and classification. Initially, the dataset is dissected, watched and screened, at that point the obtained information is handled in python programming utilizing Machine Learning Algorithm to be specific Decision Tree Algorithm and Random backwoods classifier Algorithm. SVM (Support vector machine) shows the higher outcome as far as exactness for identifying heart illness. Henceforth the proposed framework is demonstrated to be solid one for foreseeing prior heart disease. The proposed hardware as well as software system helps patient to predict heart disease in early stages. It will be helpful for mass screening system in villages where hospital facilties are not available, i.e., rural areas.


Modules


Algorithms


Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL