In a modern life, early healthcare prediction plays an important role to prevent the loss of life caused by prediction delays in treatment. Nowadays, the researchers focused on the Big data analysis, which is used to identify the future health status and provides an efficient way to overcome the issues in early prediction. Many researches are going on predictive analytics using machine learning techniques to provide a better decision making. Big data analysis provides great opportunities to predict future health status from health parameters and provide best outcomes. However, the data classification is one of the major challenging tasks due to noisy data or missing data in the dataset. Feature selection techniques play an important role in the classification process by removing irrelevant features from the extracted data. In this research work, the Rough Set Theory (RST) technique is used to select the most relevant features, which helps to provide the efficient classification of medical data and disease detection. The selected features are given as input to the Recurrent Neural Network (RNN) technique for disease prediction. The proposed method is also called as RST-RNN, where the experiments are carried out on the UCI machine learning repository dataset in terms of accuracy, f-measure, sensitivity and specificity. The results showed that the RST-RNN method achieved accuracy of 98.57%, where the existing Support Vector Machine (SVM) achieved 90.57% accuracy and Naive Bayes (NB) achieved 97.36% accuracy for heart disease dataset.