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Coronary heart diseases act as a life threatening disease where most of the people are affected. Prediction of these coronary diseases at an early time with higher rate of accuracy could be a effective solution for this problem. The places where the availability of medicos is low there the automatic prediction model plays an important role in saving the life of many people. To enhance the prediction model this paper proposed a HEOA-based LightGBM classifier for forecasting the coronary heart diseases. The preprocessing is performed using data imputation which uplifts the features of the data and the formation of feature vector strengthens the process by adding supreme features. The significance of the research is proved by effectively tuning the parameters which optimize the time period and achieves an accuracy rate of 93.064%, specificity rate of 95.618% and sensitivity rate of 91.038% .