Predicting Heart Disease using Sensor Networks, IoT, and Machine Learning: A Study on Physiological Sensor Data and Predictive Models

Context: The Internet of Things (IoT) and sensor networks are used for structural health monitoring (SHM). It can predict cardiac disease in healthcare by utilizing sensors and machine learning.

Objective: The goal of this research is to create a model for predicting cardiac disease by using sensor networks, IoT, and machine learning. To construct a prediction model, physiological data from patients will be collected and analysed using machine learning techniques.

Methodology: The methodology for this study is employing wearable sensors to collect physiological data from patients such as heart rate, blood pressure, and oxygen saturation levels. The data is subsequently processed and translated into an analysis-ready format. The most important predictors of heart disease are identified using feature selection and engineering techniques.

Statistical Measurement: A predictive model's performance can be assessed using statistical metrics, which can also assist pinpoint areas that need improvement. It's crucial to pick the right statistical measures based on the demands and objectives of the predictive model. Accuracy, precision, Recall, F1-score etc. are used for the performance of the proposed cardiac diseases model.

Conclusion: The heart disease prediction model developed in this work has the potential for improving patient outcomes while also lowering healthcare costs by identifying patients at risk of developing heart disease and offering appropriate interventions and treatments. Future research can expand on the possibilities of sensor networks, IoT, and machine learning approaches in healthcare, allowing for the development of more accurate and effective predictive models for heart disease and other medical diseases.