Feature based analysis of endometriosis using machine learning

S Visalaxi, S, Visalaxi, Sudalaimuthu Thalavaipillai, Surya Rajeswar, Pon Ramlingam
In: Indonesian Journal of Electrical Engineering and Computer Science · 2022 · vol. 29(3) , pp. 1700 · doi:10.11591/ijeecs.v29.i3.pp1700-1707 · W4311950696
article OA: diamond CC0 ⤵ 3 in-corpus citations
🔓 Open OA copy Full text JSON View on OpenAlex View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

A decision tree algorithm utilizing symptoms from laparoscopic surgery, including tissue size, predicted endometriosis with 84.85% test accuracy, identifying tissue size as the most influential feature.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

⚙ AI-generated deep summary by claude@2026-06, 2026-06-09 · read from full text ⓘ

The paper used a decision tree machine-learning approach to analyze features for predicting endometriosis severity, using symptoms identified from laparoscopic surgery as input variables. The features included mass-like structure, tissue size, variation in tissue colour, and blockages in fallopian tubes, and the tree was built using entropy and Gini index criteria. The reported performance showed Gini index outperformed with training accuracy of 84.08% and test accuracy of 84.85%. This paper is centrally about endometriosis — it focuses on machine-learning feature-based severity prediction using laparoscopic symptom attributes.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Machine learning is a cutting-edge technology used for predicting and diagnosing various diseases. Various machine learning algorithm facilitates the prediction. The decision tree belongs to learning algorithm that performs both classification and prediction. The decision tree constructs the tree-like to evaluate the best features. The decision tree performs well in the prediction of various diseases. Endometriosis is a recurrence disease that creates an emotional impact in women. Endometriosis is a lump-like structure that appears at several locations in reproductive organs of women. The diagnosis of endometriosis was predicted through scanning procedures and laparoscopic procedures. The symptoms identified from laparoscopic surgery were used as the features for predicting the severity of endometriosis. The symptoms include mass-like structure, tissue size, variation in tissue colour, and blockages in fallopian tubes. The decision tree analyze the features of endometriosis by using two criteria such as entropy and Gini index. The entropy and Gini index construct the tree by identifying the size of tissue as major influencing attributes. The Gini index outperforms well with training accuracy of 84.08% and test accuracy of 84.85.
Full text 1,821 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

Machine learning is a cutting-edge technology used for predicting and diagnosing various diseases. Various machine learning algorithm facilitates the prediction. The decision tree belongs to learning algorithm that performs both classification and prediction. The decision tree constructs the tree-like to evaluate the best features. The decision tree performs well in the prediction of various diseases. Endometriosis is a recurrence disease that creates an emotional impact in women. Endometriosis is a lump-like structure that appears at several locations in reproductive organs of women. The diagnosis of endometriosis was predicted through scanning procedures and laparoscopic procedures. The symptoms identified from laparoscopic surgery were used as the features for predicting the severity of endometriosis. The symptoms include mass-like structure, tissue size, variation in tissue colour, and blockages in fallopian tubes. The decision tree analyze the features of endometriosis by using two criteria such as entropy and Gini index. The entropy and Gini index construct the tree by identifying the size of tissue as major influencing attributes. The Gini index outperforms well with training accuracy of 84.08% and test accuracy of 84.85.

Keywords

Decision tree; Endometriosis; Entropy; Gini index; Laparoscopic surgery; Mass-like Full Text: PDFDOI: http://doi.org/10.11591/ijeecs.v29.i3.pp1700-1707 Refbacks - There are currently no refbacks. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) p-ISSN: 2502-4752, e-ISSN: 2502-4760 This journal is published by the Institute of Advanced Engineering and Science (IAES).

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Condition tags

endometriosis

Citation neighborhood (sparse)

Too few in-corpus citations on either side for a chart; here are the lists.

Cites (4)

Cited by (3)

References (26)

Cited by (3)

Source provenance

openalex
last seen: 2026-06-04T00:00:01.174412+00:00
License: CC0 · commercial use OK