Biomedical (cardiac) data mining: Extraction of significant patterns for predicting heart condition

There is a huge amount of `knowledge-enriched data´ in hospitals, which needs to be processed in order to extract useful information from it. This data is very useful in making valuable medical decisions. However, there is a lack of effective analysis tools to discover hidden relationships in data. The objective of this research is to mine the historical unstructured data of heart patients and to extract significant features and patterns. This work is based on a large amount of unstructured data in the form of patients medical reports collected from a renowned cardiac hospital in Pakistan. Firstly data preparation is done in which the unstructured (textual) data of heart patients is converted to structured (tabular) form and then pre-processed to make it suitable to apply different data mining techniques. After data preprocessing, unsupervised learning strategy is used in which K-Means clustering technique is applied to find out clusters in data which are further used to extract hidden patterns related to heart patients. These patterns can then be used for heart condition prediction besides helping medical practitioners in making intelligent verdicts. Finally, performance evaluation of k-Means with other clustering algorithms is done and results are compared.