A structural health-monitoring system needed to come out from the problem associated due to the rapidly growing population of elderly and the health care demand. The paper discussed the consumer’s electricity usage data, from the smart meter, how to support the healthcare sector by load profiling the normal or abnormal energy consumption.  For this work, the measured dataset is taken from 12 households and collected by the smart meter with an interval of an hour for one month. The dataset is grouped according to the features pattern, reduced by matrix-based analysis and classified with K-Means algorithm data mining clustering method. We showed how the clustering result of the Sum Square Error (SSE) has connection trend to indicate normal or abnormal behavior of electricity usage and leads to determine the assumption of the consumer’s health status.