Endometrium Phase prediction using K-means Clustering through the link of Diagnosis and procedure

S Visalaxi, T. Sudalaimuthu
In: 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN) · 2021 · vol. 213 , pp. 1178–1181 · doi:10.1109/spin52536.2021.9566041 · W3211243175
Other OA: closed CC0 ⤵ 3 in-corpus citations
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This study applied K-means clustering to link endometriosis diagnosis with treatment procedures, forming clusters to differentiate endometrial phases and achieving a silhouette score of 0.198.

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Abstract

The endometriosis was a malignancy reproductive problem. The tissue like structure look at the exterior side of the uterus. The endometriosis was found among 10% of global women population. Endometriosis affect other region includes pelvis, gall bladder, intestines etc. The prediction of endometriosis was done through various ways. Laparoscopic procedure is considered as standard method for visualizing the existence of endometriosis. The proposed study uses K-means clustering algorithm for linking the association of diagnosis of endometriosis with the procedure applied to treat the endometriosis. This algorithm form the clusters to differentiate the phases of endometrium at various level. The K-mean algorithm performance was analyzed using silhouette score metrics. The score was compared with principal component analysis .The K-means algorithm yields the silhouette score of 0.198.

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Condition tags

endometriosis

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

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