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K-Means Clustering for Asthma Endotypes

S. Poorani, Dr. P. Balasubramanie, Dr. D. Vimal Kumar

Abstract


Unsupervised learning algorithms are major Data mining techniques that can be used for clinical data analysis. Asthma is a constant inflammatory disease of the respiratory channels in which the reason for its prevalence is not clear. Its dominance is rising all over the world. Clustering techniques can be used to identify the hidden disease characteristics that may assist in the treatment and to create awareness about the disease. This paper implements the k-means clustering algorithm to identify the asthma endotypes and related root causes from the epidemiological data that was collected through questionnaire from asthma patients.

Keywords


Data Mining, Clustering, Partition Clustering, Clinical Data Analysis, Asthma, Endotypes, K-Means.

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References


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