Symptoms based endometriosis prediction using machine learning

S Visalaxi, Visalaxi Sankaravadivel, Sudalaimuthu Thalavaipillai
In: Bulletin of Electrical Engineering and Informatics · 2021 · vol. 10(6) , pp. 3102–3109 · doi:10.11591/eei.v10i6.3254 · W3208449664
article OA: diamond CC0 ⤵ 7 in-corpus citations
🔓 Open OA copy Full text JSON View on OpenAlex View at publisher
⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This study used chi-square analysis and correlation coefficients to identify symptoms associated with different stages of endometriosis and developed an Endometriosis Prediction Factor algorithm to predict its presence.

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, 2026-06-07 · read from full text ⓘ

The paper studies how symptom profiles could be used to predict endometriosis stage by applying chi-square analysis and correlation coefficients to determine symptom–stage associations and then building an endometriosis prediction factor (EPF) algorithm. It reports that derived EPF values greater than 1 indicate the presence of endometriosis, and it gives stage-specific symptom influence estimates (e.g., menstrual flow for mild disease, dysmenorrhea for minimal disease, tenderness for moderate disease, and adnexal mass for deep infiltrating disease). The authors do not describe dataset size, sampling, or validation performance in the provided text, which limits assessment of generalizability and predictive accuracy. This paper is centrally about endometriosis—predicting endometriosis presence and severity based on symptoms using chi-square-derived EPF modeling.

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

Abstract

Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.
Full text 9,884 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.

Keywords

Adnexal mass; Chi-square; Correlation coefficient; Dysmenorrhea; Tenderness DOI: https://doi.org/10.11591/eei.v10i6.3254 Refbacks There are currently no refbacks. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License . <div class="statcounter"><a title="hit counter" href="http://statcounter.com/free-hit-counter/" target="_blank"><img class="statcounter" src="http://c.statcounter.com/10241695/0/5a758c6a/0/" alt="hit counter"></a></div> Bulletin of EEI Stats Bulletin of Electrical Engineering and Informatics (BEEI) ISSN: 2089-3191 , e-ISSN: 2302-9285 This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) .

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

endometriosisdie_deep_infiltratingdysmenorrhea

Citation neighborhood

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. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

References (28)

Cited by (7)

Source provenance

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