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Predicting Heart Disease using Sensor Networks, IoT, and Machine Learning: A Study on Physiological Sensor Data and Predictive Models
1  Department of Computer science and engineering, school of engineering and technology, GIET University, Gunupur, Odisha, India
Academic Editor: Francisco Falcone

Abstract:

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.

Keywords: Sensor networks Internet of Things (IoT); Structural health monitoring ;Heart disease ;Physiological sensor data ; Remote patient monitoring ;Machine learning; Performance measurement
Comments on this paper
Rasmita Panigrahi
The title "Predicting Heart Disease using Sensor Networks, IoT, and Machine Learning: A Study on Physiological Sensor Data and Predictive Models" suggests a comprehensive and interdisciplinary approach to addressing a critical health issue. Here are some comments on different aspects of the proposed study.
Overall, the proposed study appears to be at the intersection of cutting-edge technologies and critical healthcare needs, offering the potential for significant advancements in the field of predictive medicine.

It is one of the best paper
SIBO PATRO
Dear Prof Panigrahi,

Thank you so much for taking the time to read my article and for your incredibly kind words! I'm thrilled to hear that you found it valuable and insightful. Your positive feedback truly means a lot to me and encourages me to continue creating content that resonates with my audience.

I'm delighted that you enjoyed the article and that it provided you with valuable information. Your support and encouragement inspire me to keep striving for excellence in my writing.

Once again, thank you for your encouraging words and for being a part of this journey. I look forward to sharing more content that you will find engaging and beneficial.

Best regards,
Dr. Sibo Prasad Patro.

SIBO PATRO
The article Predicting Heart Disease using Sensor Networks, IoT, and Machine Learning: A Study on Physiological Sensor Data and Predictive Models shows how IoT, machine learning how they are important in today's society. Looking on the mortality rate of CVD in today's world this article can help the society towards predicting the disease well in advance. The study appears to be at the intersection of cutting-edge technologies and critical healthcare needs in this modern world. offering the potential for significant advancements in the field of AI.
The study shows that the advanced technologies enable continuous monitoring of physiological data, allowing for early detection of potential heart-related issues. Early detection can lead to timely interventions and treatments, potentially preventing severe health complications.Machine learning algorithms can analyze large volumes of sensor data to create personalized health profiles for individuals. This personalized approach helps in tailoring healthcare interventions and treatments based on a person's specific health status, improving overall outcomes.Sensor networks and IoT devices facilitate remote monitoring of patients' health in real-time. This is particularly beneficial for individuals living in remote areas or those with limited access to healthcare facilities. It enables doctors to remotely assess a patient's condition and provide timely medical advice or intervention. Overall the concept behind the reserch is tremendous.



 
 
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