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During recent years, maintaining scientific and medical databases has turned out to be a major challenge in the medical sector. The medical data regarding the patients comprise diverse diagnostic medical reports and features for such disease need to be recorded carefully for providing high-quality treatments. Therefore, this paper proposes a hybrid deep convolutional neural network (HDCNN)-based COOT approach to predict the risk of diseases. Initially, an improved crossover-based levy flight optimization algorithm (ICLFDO) algorithm is employed in selecting and processing the unstructured textual data. Then later, the HDCNN-COOT approach predicts the diseases more accurately. In addition to this, the classifier predicts whether the patient is at risk of disease in the future or not. The efficiency of the proposed model is evaluated by using a dataset obtained from University Hospital of Ludwig Maximilian University of Munich, Germany. The data collected in the data center include 29,477,035 data items from 36,082 patients. The proposed model is capable of using structured data and uses textual data of the patients. Finally, the higher classification accuracy and improved performance of the classifiers can be viewed using the experimental results conducted on five different datasets. [ABSTRACT FROM AUTHOR]