Data mining approach for prediction of fibroid disease using neural networks

Uterus fibroid diagnosing could be a advanced task which needs a lot of expertise and knowledge. Diagnosing of uterine fibroids is formed by bimanual pelic examination. Traditional method of predicting fibroid disease is doctor´s examination or variety of medical tests like, MRI, Ultrasound scan, Biopsy etc., Nowadays, health care trade contains large quantity of health care knowledge that contains hidden data. This hidden information is beneficial for creating effective choices. Computer primarily based data together with advanced data mining techniques are used for acceptable results. Neural network is wide algorithm used for predicting fibroid disease diagnosing. During this analysis paper, a Fibroid Disease Prediction System (FDPS) is developed exploitation neural network. The FDPS system predicts the likelihood of patient obtaining a fibroid disease. For prediction, the system uses age, heavy bleeding, status of marriage or single, pelvic pain, etc., 10 medical parameters are used. From the result, it´s been seen that the neural network predict fibroid disease with nearly 98% accuracy.