A novel hybrid data mining approach for knowledge extraction and classification in medical databases

Over the past several years, there has been an explosion in the amount of medical data generated and subsequently collected in medical domain. Data mining techniques have been used extensively in mining the medical data. Obtaining high quality data mining results is very challenging because of the inconsistency of the results of different data mining algorithms and noise in the medical data.

This thesis presents a novel hybrid data mining approach for knowledge extraction and classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using serial and parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as serial cascaded data fusion, parallel majority-voting based neural data fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid data mining approach.

The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, Pima Indian Diabetics and ECG Heart Arrhythmia.

A comparative performance analysis of the proposed hybrid data mining approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases.