The healthcare industry is considered one of the largest industry in the world. The healthcare industry is same as the medical industries having the largest amount of health related and medical related data. This data helps to discover useful trends and patters that can be used in diagnosis and decision making. Clustering techniques like K-means, D-streams, COBWEB, EM have been used for healthcare purposes like heart disease diagnosis, cancer detection etc. This paper focuses on the use of K-means and D-stream algorithm in healthcare. This algorithms were used in healthcare to determine whether a person is fit or unfit and this fitness decision was taken based on his/her historical and current data. Both the clustering algorithms were analyzed by applying them on patients current biomedical historical databases, this analysis depends on the attributes like peripheral blood oxygenation, diastolic arterial blood pressure, systolic arterial blood pressure, heart rate, heredity, obesity, and this fitness decision was taken based on his/her historical and current data. Both the clustering algorithms were analyzed by applying them on patients current biomedical historical databases, this analysis depends on the attributes like peripheral blood oxygenation, diastolic arterial blood pressure, systolic arterial blood pressure, heart rate, heredity, obesity, cigarette smoking. By analyzing both the algorithm it was found that the Density-based clustering algorithm i.e. the D-stream algorithm proves to give more accurate results than K-means when used for cluster formation of historical biomedical data. D-stream algorithm overcomes drawbacks of K-means algorithm