Key result
The Predictis IoT and machine learning system utilizing a stacking classifier predicted cardiovascular disease risk with an F1 score of 91% for two risk levels and 80.4% for three risk levels.
Why the study?
Despite technological advancements, the global burden of cardiovascular diseases continues to rise, and technology has yet to achieve the goal of lowering mortality rates.
Does the Predictis IoT and machine learning-based system accurately predict the risk level of cardiovascular diseases?
Does the Predictis IoT and machine learning-based system accurately predict the risk level of cardiovascular diseases?
The Predictis IoT and machine learning-based system demonstrates high accuracy in predicting cardiovascular disease risk levels using real-time physiological data.
High predictive performance supports IoT-ML exploration for CVD risk stratification; hypothesis-generating and requires prospective validation before clinical use.
BACKGROUND: Despite 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. METHODS: From 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. RESULTS: To 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. CONCLUSION: The 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 REGISTRATION: Not Applicable.
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Islam et al. (2023) studied Cardiovascular disease (n=920). Predictis (IoT and ML-based stacking classifier system) vs. Base machine learning algorithms was evaluated on F1 score for two-level and three-level cardiovascular disease risk classification. The Predictis IoT and machine learning system utilizing a stacking classifier predicted cardiovascular disease risk with an F1 score of 91% for two risk levels and 80.4% for three risk levels.
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