In health science, there are several researches and applications are developed using data mining techniques. This paper, contributes an idea to detect the heart disease and its co-morbid conditions along with the risk using data mining techniques. In recent scenario, health issues are huge, due to this nature predicting and classifying into different conditions are very tedious. The field of data mining has involved in those domains to predict and to classify the abnormality along with its risk level. The previous studies have used several features to diagnosis the disease, which has been collected from patients. By applying different data mining algorithms, the patient data is taken for the experiment. The main drawbacks of the previous studies are that need accurate and more number of features. In this paper, a Data mining model has been developed using Weighted Principle Analysis (WPCA) and Genetic algorithm (GA) to improve the prediction accuracy and to investigate the risk level of the disease. The proposed technique helps to the medical domain for predicting heart diseases with its various co-morbid conditions. The study proposed a new classification and prediction scheme for Heart disease data. The system has two main objectives, which are improving diagnosis accuracy and reducing classification delay. The WPCA represents with the effective splitting criteria which has been verified by the genetic algorithm.