Hemodialysis mining and patients intelligent clustering technologies

Background: Medical information systems collect vast amount of monitored clinical data. Interpreting the portions of the data that are relevant to the identification of a specific clinical problem can become a hard task. Data mining are largely used in a very wide range of applications. Data mining mainly depends on mathematical algorithms and analytical skills to drive the desired results from the huge database sets and/or collections. Clustering is one of the most important data mining techniques. Most of the earlier work on clustering has focused on numerical relationships between the values of the attributes, and ignored the inherent meaning of the values. Aim: In this work, an enhancement is added to the k -means algorithm for clustering data. Material & Methods: Furthermore, modification of the difference values between the attributes was done. The proposed clustering technique has been used to improve the quality, efficiency of health services and decision making in hemodialysis centers. Long experimentations and heavy tests were done on a variety of clustered different attributes for hemodialysis patient information systems. Results: The results showed that, our enhancement on the k -means algorithm has realized a better maximum distance and separate values for each cluster lower than the traditional k -means algorithm. Conclusion: The decision making for the session period and blood rate has been improved and made more accurate. This provides the robust and best dialysis adequacy for the specific patient case.