An Intellectual Methodology for Secure Health Record Mining and Risk Forecasting Using Clustering and Graph-Based Classification

The objective of the research work is to analyze and validate health records and securing the personal information of patients is a challenging issue in health records mining. The risk prediction task was formulated with the label Cause of Death (COD) as a multi-class classification issue, which views health-related death as the "biggest risk." This unlabeled data particularly describes the health conditions of the participants during the health examinations. It can differ tremendously between healthy and highly ill. Besides, the problems of distributed secure data management over privacy-preserving are considered. The proposed health record mining is in the following stages. In the initial stage, effective features such as fisher score, Pearson correlation, and information gain is calculated from the health records of the patient. Then, the average values are calculated for the extracted features. In the second stage, feature selection is performed from the average features by applying the Euclidean distance measure. The chosen features are clustered in the third stage using distance adaptive fuzzy c-means clustering algorithm (DAFCM). In the fourth stage, an entropy-based graph is constructed for the classification of data and it categorizes the patient's record. At the last stage, for security, privacy preservation is applied to the personal information of the patient. This performance is matched against the existing methods and it gives better performance than the existing ones.