Ten-Year Risk of Fatal Cardiovascular Disease and its Association with Metabolic Risk Factors Among Waste Pickers in South Africa

Made, Felix; Nonterah, Engelbert A.; Tlotleng, Nonhlanhla; Ntlebi, Vusi; Naicker, Nisha

doi:10.21203/rs.3.rs-236769/v1

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…The outcome result of the multivariate regression model outperforms other methods. Made et al (34) surveyed CVDs estimating 10-year risk in the different age groups of people. They proposed a SCORE model that uses risk factors, such as weight, BP, cholesterol level, smoking habit, diabetes, and many more.…”

Section: Related Study Literature Surveymentioning

confidence: 99%

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Shah

Ahirrao

Pandya

et al. 2021

Front. Public Health

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Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

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Section: Related Study Literature Surveymentioning

confidence: 99%