A Predictive Analysis on Medical Data Based on Outlier Detection Method Using Non-Reduct Computation

Authors:
Faizah Shaari;Azuraliza Abu Bakar;Abdul Razak Hamdan
Affiliations:
Polytechnic Sultan Idris Shah, Polytechnics Malaysia, Ministry of Higher Education, Sungai Lang, Selangor 45100;Center of Artificial Intelligence Technology, Faculty of Information Science and Technology, National University of Malaysia, Bangi, Selangor 43600;Center of Artificial Intelligence Technology, Faculty of Information Science and Technology, National University of Malaysia, Bangi, Selangor 43600
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

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Abstract

In this research, a new method to predict and diagnose medical dataset is discovered based on outlier mining method using Rough Sets Theory (RST). The RST is used to generate medical rules, while outliers are detected from the rules to diagnose the abnormal data. In detecting outliers, a computation of set of attributes or known as Non-Reduct is proposed by proposing two new formula of Indiscernibility Matrix Modula(iDMM D) and Indiscernibility Function Modulo (iDMFM D) based on RST. The results show that the proposed method is a fast detection method with lower detection rate. In conclusion, the computation of the Non-Reduct is expected to give medical knowledge that able to predict abnormality in dataset that could be used in medical analysis.