Newfangled Approach for Early Detection and Prevention of Ischemic Heart Disease using Data Mining

2019 ◽  
Author(s):  
Dhara B. Mehta ◽  
Nirali C. Varnagar
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Early Detection ◽  
Ischemic Heart Disease ◽  
Ischemic Heart ◽  
Using Data ◽  
Early Detection And Prevention

scholarly journals Predictive measure for Ischemic Heart Disease among Workers in Jakarta, Indonesia

2021 ◽  
Vol 31 (4) ◽  
pp. 8
Author(s):  
Leli Hesti Indriyati ◽  
Gea Pandhita S ◽  
Nurhayati Anis ◽  
Anna Suraya
Keyword(s):  
Heart Disease ◽  
High Risk ◽  
Early Detection ◽  
Ischemic Heart Disease ◽  
Prediction Models ◽  
Risk Measure ◽  
Ischemic Heart ◽  
Cardiovascular Risk Prediction ◽  
Predictive Risk ◽  
Surrounding Areas

<p>Ischemic Heart Disease (IHD) is one of the leading causes of morbidity and mortality in many countries, including Indonesia. Therefore, cardiovascular risk-prediction models are required in clinical practice for early detection in high-risk populations, including the worker population. This study intends to develop a predictive risk measure for early detection of IHD incidences among employees in Jakarta, Indonesia. Source of data was the database of 4,100 medical check-up (MCU) results of employees and entrepreneurs in Jakarta and surrounding areas in January to October 2019. Multivariate analysis showed that being aged &gt;40 years (p=0.000; OR=5.329 (95% CI 2.621-10.833)), having a history of dyspnea (p=0.000; OR=5.699 (95% CI 2.524-12.871)), smoking (p=0.048; OR=2.007 (95% CI 1.924-4.359)) and HDL&lt;50 mg/dL (p=0.049; OR=1.811 (95% CI 1.099-3.281)) were all good predictors to detect IHD in the worker population. The combination of these predictors results with a cut-off point of 2.5, showed accuracy (79.2% sensitivity and 66.3% specificity). Workers who have a score &gt;2.5 are at high risk of developing IHD in the future. This scoring system can be used by workers or companies to take early preventive measures.</p>

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