Related Experiment Video
Updated: Feb 4, 2026
In Silico Clinical Trials for Cardiovascular Disease
Published on: May 27, 2022
Triboostcardio ensemble model for cardiovascular disease detection using advanced blockchain-enabled health
M Mayuranathan1, V Anitha2, P Nehru3
1Department of Computer Science and Engineering, SRM Valliammai Engineering College, Chengalpattu, Tamil Nadu, India.
Insights
This study introduces an advanced model for early heart disease detection using IoT wearables and blockchain for secure data management. The framework significantly improves predictive accuracy and timely intervention for cardiovascular diseases (CVDs).
Area of Science:
- Biomedical Engineering
- Health Informatics
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular diseases (CVDs) are a leading cause of global mortality, necessitating improved early detection and management strategies.
- Traditional monitoring systems lack real-time analysis and individual insights, leading to delayed interventions and data privacy concerns in digital healthcare.
Purpose of the Study:
- To develop an integrated model for early cardiovascular disease (CVD) detection using Internet of Things (IoT) wearable devices, electronic health records, and blockchain technology.
- To enhance the accuracy and timeliness of CVD detection while ensuring robust data privacy and security.
Main Methods:
- Integration of IoT-based wearable devices for physiological data collection and electronic clinical records.
- Application of Kalman filtering for data reliability, and shallow/deep feature extraction for pattern identification.
- Utilizing Refracted Sand Cat Swarm Optimization (SCSO) for feature maximization and a TriBoostCardio Ensemble model (CatBoost, AdaBoost, LogitBoost) for classification.
- Implementation of blockchain and smart contracts for secure and transparent access control to health information.
Main Results:
- The proposed framework demonstrates enhanced predictive accuracy for early cardiovascular disease detection compared to traditional methods.
- The combination of SCSO feature selection and the TriBoostCardio Ensemble model significantly improves model robustness and classification precision.
- Experimental results validate the framework's ability to detect CVDs earlier and more accurately.
Conclusions:
- The developed framework offers a secure, accurate, and timely solution for cardiovascular disease detection, addressing critical data privacy and integrity issues.
- The integration of advanced optimization techniques, ensemble learning, and blockchain technology provides a trustworthy advancement for modern healthcare systems.
Introduction:
Heart diseases (CVDs) are a major cause of morbidity and mortality in all global regions and thus there is the pressing need to develop early detection and effective management approaches. Traditional cardiovascular monitoring systems do not necessarily have real-time analyzing solutions and individual understanding, which leads to delayed interventions. Moreover, one of the greatest issues in digital healthcare applications remains to be data privacy and security.
Methods:
The proposed research is to present a developed model of CVD detection that will combine Internet of Things (IoT)-based wearable devices, electronic clinical records, and access control using blockchain. The system starts by registering patients and medical personnel and then proceeds with collecting physiological as well as clinical data. Kalman filtering helps in improving data reliability in the pre-processing stage. Shallow and deep feature extraction methods are used to describe complicated patterns of data. A Refracted Sand Cat Swarm Optimization (SCSO) algorithm is used as part of feature maximization. A new TriBoostCardio Ensemble model (CatBoost, AdaBoost, and LogitBoost) is used to conduct the classification task and enhance the predictive accuracy. Smart contracts provide safe and transparent access to health information.
Results:
There are experimental results that the proposed framework enhances high predictive accuracy and detecting cardiovascular diseases earlier than traditional ones. The combination between SCSO feature selection and the TriBoostCardio Ensemble model improves the sturdiness of the model and precision of classification.
Discussion:
Besides the fact that the presented framework promotes the accuracy and timeliness of CVD detection, it also way to deal with important problems related to the data privacy and integrity with the help of blockchain-based access control. This solution offers a stable and trustworthy solution to the current healthcare systems with the combination of the smart optimization of features, ensemble learning, and secure data management.
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