PREDICTING POLYCYSTIC OVARY SYNDROME THROUGH MACHINE LEARNING TECHNIQUE USING PATIENTS’ SYMPTOM DATA AND OVARY ULTRASOUND IMAGES

Polycystic ovary syndrome (PCOS) is the most prevalent endocrinological abnormality & one of the primary causes of anovulatory infertility in women globally. The real-world clinical PCOS detection technique is critical since the accuracy of interpretations being substantially dependent on vast num...

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Bibliografiske detaljer

Hovedforfatter:	ALAM SUHA, SAYMA
Format:	Thesis
Sprog:	engelsk
Udgivet:	Department of Computer Science and Engineering, MIST 2024
Fag:	PREDICTING POLYCYSTIC OVARY SYNDROME THROUGH MACHINE LEARNING TECHNIQUE USING PATIENTS’ SYMPTOM DATA AND OVARY ULTRASOUND IMAGES
Online adgang:	http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/776
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