Abstract—Heart disease has remained the leading cause of death among Malaysians for 13 years from 2005 to 2017. As it has become the prominent factor of death in Malaysia, the intention is to improve the accuracy of query for cardiology related cases as it is the primary source of analytical data for heart disease. Choosing the right terminology is one of the criteria to improve the accuracy as the clinical term can be mapped as much as possible. Therefore, Systematized Nomenclature of Medicine Clinical Term (SNOMED CT) has been selected for implementation as it is known as the most comprehensive, multilingual clinical healthcare terminology in the world. This paper presents the implementation to enrich and increase the result accuracy by automatically mapping the Clinical Discharge Summary using several techniques in Natural Language Processing (NLP) with SNOMED CT. By observing the trend and pattern of data, a facility or ministry can plan one step ahead, through prevention or future planning. Therefore, the accuracy of the result is the key factor to derive the outcome.