Ischemic Heart Disease Prediction on Echocardiography Data using Binary Logistic Regression Model
Background: Ischemic Heart Disease (IHD) has been taxing the healthcare systems with a huge economic burden by being the major cause of deaths globally. In that, India contributes to about one-fifth of such deaths. In the present study, we evaluated individual contributions of M-Mode 2D echocardiographic parameters to determine the presence of IHD in a large segment of the Indian population using a logistic model.
Methodology: A total of 7304 echo records were selected for performing the logistic regression from Electronic Health Records (EHRs) at JSS Hospital. The data set included 6191 patients without IHD and 1113 patients with IHD. The study included one dichotomous variable and fifteen explanatory variables that were taken during the echocardiography examination.
Results: This study is the first to apply a large sample from echo data, to determine how well a predictive model would perform based only upon patients M-Mode echocardiography measurements without clinical risk factors or physical exam findings. All the variables exhibited statistically significant variation between IHD patients and non-IHD patients.
Conclusion: The resulting model has a higher accuracy rate (96.7%), which makes it a handy tool for junior cardiologists to screen the patients who have a high probability of having the disease.