A Comparative Study on Prediction of Endometriosis Causing Infertility Using Machine Learning Techniques: in Detail
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Abstract
The purpose of this study is to utilize Artificial Intelligence to analyse and predict endometriosis problem in women. All traditional methods are used before to develop or to predict the likelihood of endometriosis based on the symptoms presented. By identifying the symptoms of endometriosis, the machine learning algorithms can determine the type of endometriosis and the appropriate course of action for patients. This technology can be used to educate women globally on the signs and symptoms of endometriosis and help them take preventive measures to avoid this deadly disease. The results of this research demonstrate the potential of advanced technology to revolutionize healthcare by providing early detection and treatment options for endometriosis. In areas with limited access to medical care, this tool can aid in identifying ovarian cancer and reducing mortality rates. By detecting and diagnosing endometriosis at an early stage, this program can play a significant role in promoting women's health and wellbeing. The methodology proposed in this study produces classification results that are on par with cutting-edge deep learning techniques. In addition, the methodology provides visual explanations that offer valuable insights into the inner workings of each model and enhance the accuracy and reliability of the predictions.
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References
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