As healthcare has become more data-driven in recent years, the amount of data produced has increased. Digital data can take the form of audio, pictures, videos, transcripts, clinical records, electronic medical records, and free text. More and more clinical records are being created as a result of the development of information technology systems, and these records need to be processed and examined. Examining and interpreting medical data can be a challenging task that takes a significant amount of time, resources, and human effort. It takes a medical expert to complete the laborious work of assessing a large volume of data. Therefore, artificial intelligence technologies are being used to analyze data in healthcare. The main aim is to build a multi-label classification system that predicts the morbidities that may occur in the future taking clinical notes as input. BERT model exploiting transformer architecture is used to deal with constraints of the small datasets and improve the performance of the model.