Prediction of diseases by researchers in healthcare industries and the different machine learning algorithms involved play an important role in disease prediction. Similarly, the prediction of patient trajectory data and its analysis also plays a vital role. A novel deep learning approach named Deep Concept Specific learning has been proposed for analyzing unstructured patient trajectory data. The proposed model is structured as a generic model suitable for any patient's medical disease. To evaluate the proposed model performance, the disease related to diabetes has been employed. It implies for autoencoder model, which has been optimized with hyper parameter tuning. It effectively transforms high dimensional features into low dimensional feature space to extract concept-specific features. It is implemented on the Further, all the parameters employed in the sparse features are manipulated. They are considered a loss and encoder function to compute the optimal features for effectively predicting the results. The encoder function maps the data points containing optimal features into latent representations based on disease characteristics. It is done from the patients' perspectives by the ReLu activation function based on generated feature characteristics.