  Worldwide research shows that millions of lives lost per year because of
heart disease. The healthcare sector produces massive volumes of data on heart
disease that are sadly not used to locate secret knowledge for successful
decision making. One of the most important aspects at this moment is detecting
heart disease at an early stage. Researchers have applied distinct techniques
to the UCI Machine Learning heart disease dataset. Many researchers have tried
to apply some complex techniques to this dataset, where detailed studies are
still missing. In this paper, Principal Component Analysis (PCA) has been used
to reduce attributes. Apart from a Hybrid genetic algorithm (HGA) with k-means
used for final clustering. Typically, the k-means method is using for
clustering the data. This type of clustering can get stuck in the local optima
because this method is heuristic. We used the Hybrid Genetic Algorithm (HGA)
for data clustering to avoid this problem. Our proposed method can predict
early heart disease with an accuracy of 94.06%.
