Heart disease commonly occurring disease and is the major cause of sudden death nowadays. This disease attacks the persons instantly. Most of the people do not aware of the symptoms of heart disease. Timely attention and proper diagnosis of heart disease will reduce the mortality rate. Medical data mining is to explore hidden pattern from the data sets. Supervised algorithms are used for the early prediction of heart disease. Nearest Neighbor (KNN) is the widely used lazy classification algorithm. KNN is the most popular, effective and efficient algorithm used for pattern recognition. Medical data sets contain a large number of features. The Performance of the classifier will be reduced if the data sets contain noisy features. Feature subset selection is proposed to solve this problem. Feature selection will improve accuracy and reduces the running time. Particle Swarm Optimization (PSO) is an Evolutionary Computation (EC) technique used for feature selection. PSO are computationally inexpensive and converges quickly. This paper investigates to apply KNN and PSO for prediction of heart disease. Experimental results show that the algorithm performs very well with 100% accuracy with PSO as feature selection.