Data mining techniques are useful for analyzing data from many different dimensions and for identifying relationships. Association between patient characteristics and length of hospital stay using data mining technique was my ultimate goal. Data were collected from hospital records. Convenient sampling technique was used to collect data on the basis of inclusion and exclusion criteria and written consent was taken prior to interview. Descriptive as well as inferential statistics such as Chi-square were used to present data and show association. Nearly half (48%) of the patients were diagnosed as aortic sclerosis. Compressive chest pain, hypertension, intermittent chest pain, respiratory distress, central chest tightness, shortness of breath, sweating was major symptom. Moreover, mean hemoglobin was 12.25 gm/dl. In addition to this most of the patients stayed <2 days in hospital. No significant association was found between age group and length of hospital stay as well as sex. Statistically significant association was found between hemoglobin level and length of hospital stay. Every hospital should use their data-by-data mining technique to do better management of patients. If it could be done on a large scale the result would be more precise.