Risk Assessment Using Local Outlier Factor Algorithm
نویسندگان
چکیده
In this paper we introduce the unsupervised machine-learning algorithm named Local Outlier Factor (LOF), for health risk assessment. In general the LOF algorithm is used with numerical attributes and the outcome of the algorithm is parting the patterns into normal and abnormal events. In this paper we introduce the extended LOF algorithm with three experimental contributions: (i) utilization of complex nominal attributes, (ii) the developed methodology for detecting the level of event anomaly (low risk, medium risk and high risk) and (iii) providing the information about the risk status for each analysed parameter.
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