Hybrid Approach to Outlier Detection in Medical Dataset
DOI:
https://doi.org/10.51983/ajcst-2017.6.2.1785Keywords:
K-means clustering technique, density-based algorithm, trajectory outlier detection (TRAOD), Heart disease datasetAbstract
Outlier detection has been a very important concept in the realm of data analysis and the complex relationships that appear with regard to patient symptoms, diagnoses and behavior are the most promising areas of outlier. The data typically consists of records which may have several different types of features such as patient age, blood group and weight. Recently, density-based outlier detection has emerged as a viable and scalable alternative to traditional statistical and geometric approaches. Density Based Outlier Detection Algorithm along with K-means partition technique is used for detecting outliers in Heart Disease dataset which is used to diagnose the abnormal data. This analysis can be used by doctor to predict heart disease of particular patient.
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