Remote Predictive Heart Disease Management By Using Machine Learning
Keywords:
Heart Disease Prediction, Remote Monitoring, Patient Health, Early Detection, Cardiac Risk, Personalized Healthcare.Abstract
The cardiovascular diseases continue to be among the top mortality factors worldwide
because of the delay in diagnosis or lack of continuous monitoring of people at
risk. Further, one of the major reasons for such challenges is early detection since
conventional techniques are mostly based on periodic clinical visits with manual
interpretation of complex physiological data, which can easily lead to misdiagnosis or
delayed interventions. Herein, this project presents a system, Remote Predictive Heart
Disease Management, which proposes using CNNs for automatic and accurate prediction
of heart disease. The system collects all critical physiological parameters related to ECG
signals, heart rate, blood pressure, and other clinical measures. These are preprocessed
and analyzed through a CNN model implemented using MATLAB. Having enabled remote
monitoring, clinicians will be able to monitor patients’ health in real time and predict
cardiac events in advance. The proposed approach is novel in the way it combines CNN
based predictive analytics with continuous remote patient monitoring for personalized healthcare with timely interventions and reduced hospitalization.
