Abstract

The fast growth of medical records provides new opening for meaningful information retrieval in clinical diagnosis and treatment. Although nursing and pathology records provide a complete account of patient's information they are not fully utilized while taking major decisions of surgery or chemo therapy on patients. This research proposes a Minimum spanning tree algorithm to develop k-clusters of training data related to different liver diseases which are validated using Silhouette coefficient. A text classification algorithm is developed using cluster centers as training samples which uses a similarity measure to classify the categorical data. Simulation results show that the algorithm proposed can lower the calculation complexity and improve the accuracy of established text classification algorithms like k-NN. This research can serve as a medical diagnosis tool for classifying patient records and reveal important vocabularies that characterize nursing and pathology records.

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