Machine Learning based Cardiovascular Disease Pattern Prediction Technique for Remote Healthcare Monitoring Systems

Author(s):

Subhasini SV* and S Raja Mohamed

Wearable devices and the various applications of wearable devices are increasing greatly which encourages researchers to focus much on Internet of Medical Things (IoMT). The IoMT is playing a major and considerable role for reducing mortality rates as IoMT is helping diseases pattern well in advance. As we know, the cardiovascular disease threatens us as mortality is relatively high which is observed from the available literature survey. It was noticed from the statistical report that there were relatively high rate of heart diseases registered. The healthcare system is needed for the better cardiovascular diseases prediction techniques, which will help medical practitioners to predict this disease well in advance that will facilitate early prevention, detection, and fruitful treatment to patients. This will save human life and can reduce mortality rate. As we know, machine learning models and the internet of medical things jointly enabled methodologies for supporting healthcare services particularly for cardiovascular disease pattern prediction, classification and accurate diagnosis. It was noticed that there were two IoMT based models proposed recently. They are Bagging- Fuzzy-Gradient Boosting Decision Tree (FGBDT) and Hybrid Random Forest-Linear Model (HRFLM). These two models were implemented and analyzed their performances during training and testing processes in terms of prediction accuracy, precision, sensitivity, specificity, FScore and average processing time (ms) and it was noticed that the Hybrid Random Forest- Linear Model (HRFLM) is performing well in terms of accuracy and time complexity whereas Bagging-Fuzzy-Gradient Boosting Decision Tree (FGBDT) is performing well in terms of precision, sensitivity, specificity and FScore. To maximize classification and prediction accuracy better, this work is proposed an efficient Ensemble Support Vector classifier-Weighted Random Forest called ensemble SVC-WRF (E-SVC-WRF) and implemented for analysis. From the experimental results, it was noticed that the proposed Ensemble Support Vector classifier-Weighted Random Forest called ensemble SVC-WRF (E-SVC-WRF) is performing well as compared with Hybrid Random Forest-Linear Model (HRFLM) and Bagging- Fuzzy-Gradient Boosting Decision Tree (FGBDT) in terms of prediction accuracy, precision, sensitivity, specificity, FScore and average processing time (ms).