An Efficient Decision Tree Establishment and Performance Analysis with Different Machine Learning Approaches on Polycystic Ovary Syndrome
Aroni Saha Prapty, Tanzim Tamanna Shitu
Abstract
Polycystic Ovary Syndrome (PCOS) is an exceedingly serious disease for which a woman has to pay a lot of lifelong damages. A woman does much suffering either not knowing that she is affected by it or that it is not caught at a very early stage. This is a treatable cause of infertility and affects a woman's health in many ways like metabolic syndrome, sleep apnea, depression even endometrial cancer. But if she notices it at the beginning all these can be avoided under careful supervision. By applying different methods of machine learning and an efficient decision tree is established based on the best performer. After learning once from the doctor that she is affected by PCOS, from the next time she can verify the extent of some of the most responsible noticeable changes in the body and hormonal tests using that tree. Finally, she can decide when to see a doctor or everything is under control.