Prognosis of prostate cancer by artificial neural networks
Expert Systems with Applications: An International Journal
Uncovering age-specific invasive and DCIS breast cancer rules using inductive logic programming
Proceedings of the 1st ACM International Health Informatics Symposium
Predicting asthma outcome using partial least square regression and artificial neural networks
Advances in Artificial Intelligence
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In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians.