Predictis: an IoT and machine learning-based system to predict risk level of cardio-vascular diseases

BMC Health Services Research

Research

Muhammad Nazrul Islam¹  Kazi Rafid Raiyan¹  M. M. Rushadul Mannan¹  Tasfia Tasnim¹  Asima Oshin Putul¹  Shutonu Mitra¹  Angshu Bikash Mandol¹ 

[1]Department of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka-1216, Bangladesh

关键词: Cardio-vascular disease;    Internet of Things;    Machine learning;    Stacking classifier;    Prediction;    Risk level;   

DOI  :  10.1186/s12913-023-09104-4

 received in 2022-08-10, accepted in 2023-01-25,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

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