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Endometriosis Early Detection

:מספר הפרויקט
Daniel Moshe, Adi Haber
:שמות הסטודנטים המציגים
פרופ' שרייבמן עדי וד"ר שוויקי דורית
:שם המנחה
סדנת נתונים רפואיים
:שם הסדנה
מסלול אלגוריתמי/מחקרי
:מסלול הסדנה
:תקציר הפרויקט

Our goal is to use machine learning to help with early endometriosis detection, using patient data found in the UK Biobank. We researched common indicators and risk factors for endometriosis and will use them as the features of our selected model.

Endometriosis, a chronic inflammatory condition, is caused by tissue resembling uterine lining growing outside the uterus. Common symptoms are pain and infertility. Endometriosis primarily affects women of reproductive age, with approximately 180 million cases worldwide.

Diagnosing endometriosis is a challenge, as it may not always be visible through ultrasound or MRI. It often requires an invasive procedure for a definitive diagnosis. We aim to create a machine-learning model that detects endometriosis based on symptoms, making the diagnosis process simple, short, and non-invasive.

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