Author: Vishwakarma, V.; Chethan, S.; Datla, M. T.; Aqib, M. M.; Roy, S.; Thasni, T.
Title: Prediction of Severity of Polycystic Ovarian Syndrome Using Artificial Neural Networks Cord-id: 6l4l7cbk Document date: 2022_1_1
ID: 6l4l7cbk
Snippet: Artificial Intelligence applied to medical diagnosis provides numerous benefits and simultaneously contributes to the evolution and acceleration of the healthcare sector. Polycystic Ovary Syndrome (PCOS) is a common health problem that is speculated to be caused by an imbalance of androgens and insulin levels. An estimated 116 million women suffer from PCOS worldwide. From hirsutism and baldness, PCOS can contribute to chronic health problems like type 2 diabetes and heart diseases. In this pape
Document: Artificial Intelligence applied to medical diagnosis provides numerous benefits and simultaneously contributes to the evolution and acceleration of the healthcare sector. Polycystic Ovary Syndrome (PCOS) is a common health problem that is speculated to be caused by an imbalance of androgens and insulin levels. An estimated 116 million women suffer from PCOS worldwide. From hirsutism and baldness, PCOS can contribute to chronic health problems like type 2 diabetes and heart diseases. In this paper, Artificial Neural Networks were employed to diagnose if a patient is suffering from PCOS obtaining an accuracy of 87.96%. Based on the severity of the condition, a routine is generated from the database. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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