ArticleBMC health services research2023
Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases.
Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 61 citations in OpenAlex.
- Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks.Bioengineering (Basel, Switzerland) · 2026Article
- An IoT based remote medical diagnosis system using one time pad cipher over MQTT protocol.Scientific reports · 2025Article
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- Key factors in predictive analysis of cardiovascular risks in public health.Scientific reports · 2025Article
- Early heart disease prediction using feature engineering and machine learning algorithms.Heliyon · 2024Article
- Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.BMC medical informatics and decision making · 2024Article
- Heart failure patients monitoring using IoT-based remote monitoring system.Scientific reports · 2023Article
- Article
- AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia.Journal of personalized medicine · 2023Article
- Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite technological advancement in the field of healthcare, the worldwide burden of illness caused by cardio-vascular diseases (CVDs) is rising, owing mostly to a sharp increase in developing nations that are undergoing fast health transitions. People have been experimenting with techniques to extend their lives since ancient times. Despite this, technology is still a long way from attaining the aim of lowering mortality rates.
methodsFrom methodological perspective, a design Science Research (DSR) approach is adopted in this research. As such, to investigate the current healthcare and interaction systems created for predicting cardiac disease for patients, we first analyzed the body of existing literature. After that, a conceptual framework of the system was designed using the gathered requirements. Based on the conceptual framework, the development of different components of the system was completed. Finally, the evaluation study procedure was developed taking into account the effectiveness, usability and efficiency of the developed system.
resultsTo attain the objectives, we proposed a system consisting of a wearable device and mobile application, which allows the users to know their risk levels of having CVDs in the future. The Internet of Things (IoT) and Machine Learning (ML) techniques were adopted to develop the system that can classify its users into three risk levels (high, moderate and low risk of having CVD) with an F1 score of 80.4% and two risk levels (high and low risk of having CVD) with an F1 score of 91%. The stacking classifier incorporating best-performing ML algorithms was used for predicting the risk levels of the end-users utilizing the UCI Repository dataset.
conclusionThe resultant system allows the users to check and monitor their possibility of having CVD in near future using real-time data. Also, the system was evaluated from the Human-Computer Interaction (HCI) point of view. Thus, the created system offers a promising resolution to the current biomedical sector.
trial registrationNot Applicable.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.