Key result
The M4CVD mobile machine learning system successfully classified cardiovascular disease risk with an accuracy of 90.5% in a proof-of-concept evaluation using 200 synthetic patient records.
Why the study?
Does the M4CVD mobile machine learning system accurately classify CVD risk using synthetic patient data?
Does the M4CVD mobile machine learning system accurately classify CVD risk using synthetic patient data?
A proof-of-concept mobile machine learning model using wearable sensor data and clinical databases achieved 90.5% accuracy in classifying CVD risk on synthetic data.
Warrants no clinical adoption yet; leaves open prospective validation of mobile ML for CVD risk.
In this paper we present M4CVD: Mobile Machine Learning Model for Monitoring Cardiovascular Disease, a system designed specifically for mobile devices that facilitates monitoring of cardiovascular disease (CVD). The system uses wearable sensors to collect observable trends of vital signs contextualized with data from clinical databases. Instead of transferring the raw data directly to the health care professionals, the system performs analysis on the local device by feeding the hybrid of collected data to a support vector machine (SVM) to monitor features extracted from clinical databases and wearable sensors to classify a patient as “continued risk” or “no longer at risk” for CVD. As a work in progress we evaluate a proof-of-concept M4CVD using a synthetic clinical database of 200 patients. The results of our experiment show the system was successful in classifying a patient's CVD risk with an accuracy of 90.5%.
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Boursalie et al. (2015) studied Cardiovascular disease (n=200). M4CVD (Mobile Machine Learning Model) was evaluated on Classification of CVD risk (accuracy). The M4CVD mobile machine learning system successfully classified cardiovascular disease risk with an accuracy of 90.5% in a proof-of-concept evaluation using 200 synthetic patient records.
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