Coronary illness can be treated as one of the major causes for mortality globally. On-time and Precise conclusion on the type of disease is significant for therapy and breakdown expectancy. Research scientists are working rigorously in their respective fields to reduce the death rate. Even though lot of research took place on this area still there is a scope for increasing the prediction accuracy. The fundamental point of our proposed work is to build up a hybrid methodology using genetic algorithm (GA) with (RBF) radial basis function (GA-RBF) for the detection of coronary sickness with increased accuracy using the feature selection mechanism. The proposed system performance achieved an accuracy of 85.40% using 14 attributes, and the prediction accuracy increased to 94.20% with nine characteristics where the functionality of the proposed system performed much better after attribute reduction.

冠心病可被视为全球范围内导致死亡的主要原因之一。及时准确地得出有关疾病类型的结论，对治疗和预期击倒率均具有重要意义。研究科学家正在各自领域进行严格的工作，以降低死亡率。即使对该领域进行了大量研究，仍然存在提高预测准确性的余地。我们提出的工作的基本点是建立一种使用遗传算法（GA）和（RBF）径向基函数（GA-RBF）的混合方法，以使用特征选择机制以更高的准确性检测冠心病。拟议的系统性能使用14个属性实现了85.40％的精度，并且预测精度提高到94。