CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

Author
Keywords
Abstract
Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication and technology. These techniques can help to save the life of millions in the medical assisted environment and can be utilized in health-care system where health expertise is less available. Fast food consumption increased from the past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more death than any other disease globally. The objective of this research is to analyze the available data pertaining to cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of available medical sensors to predict cardiovascular diseases on a real-time basis.
Year of Publication
9998
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
In Press
Issue
In Press
Number
In Press
Number of Pages
1-9
Date Published
09/2021
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2021-09/ip2021_09_002_0.pdf
DOI
10.9781/ijimai.2021.09.002
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