A Modeling Approach to Predict Risk of Coronary Heart Disease using Logit and CAP Curves

Abstract

On a global spectrum of healthcare anomalies, coronary heart disease enjoys its fair share of incidence levels and disease burden rates. The inevitability of this condition is apparent due to changing lifestyles of human beings. However, the unpredictable nature of this condition makes it a menace for management and intervention systems around the world. The study aims at generating a binary classification model sturdy enough to recognize the potent risk factors contributing towards this risk. This modeling approach could sizably reduce the area of focus to allow accurate causation insights and quantify relationships between the regressors and the outcome variables. By doing so, action plans could be created in order to deal with these red flags so obtained, ultimately aiming towards better health outcomes for people.