Heart Disease Prediction Using Machine Learning
DOI:
https://doi.org/10.32628/CSEIT2063149Keywords:
Machine Learning, Heart Disease, IoTAbstract
Heart diseases are the foremost common explanation for death worldwide over the previous couple of decades even in well developed countries like the US. About 610,000 individuals die of heart disease conditions within the US each year–that’s one in each four deaths. IoT and machine learning play a crucial role in the health care system. It is difficult to spot heart disease due to several contributory risk factors like diabetes, high blood pressure, high cholesterol, abnormal pulse, and lots of other factors. In this study, a tentative design heart disease prediction system has been proposed to detect nearing heart disease using Machine learning techniques and IoT. On a regular basis, a large amount of data is processed in health and medical sectors. Using data mining, facts and knowledge can be extracted from the data and can be used for training the machine learning model. The model uses sensors to measure various parameters of the patient like blood pressure, pulse rate and temperature. Patients cannot consult a doctor for 24 hours as it needs more time and resources. The live data of the patient is taken from the sensors and it is processed by the machine learning model to predict the chances of the patient suffering from the heart disease. If the patients have high probability, then they can contact their doctor immediately and start the treatment without much loss of time.
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