Discovery of Hidden Patterns in Breast Cancer Patients, Using Data Mining on a Real Data Set. - ATASHI, Alireza; TOHIDINEZHAD, Fariba; DORRI, Sara; NAZERI, Najmeh; GHOUSI, Rouzbeh; MARASHI, Sina; HAJIALIASGARI, Fatemeh (Studies in Health Technology & Informatics, 2019)
The aim is to recognize the unknown atterns in a real breast cancer dataset using data mining algorithms as a new method in medicine. Due to excessive missing data in the collection only data on 665 of 809 patients were available. The other missing values were estimated using the EM algorithm in SPSS21 software. Fields have been converted into discrete fields and finally the APRIORI algorithm has been used to analyze and explore the unknown patterns. After the rule extraction, experts in the field of breast cancer eliminated redundant and meaningless relations. 100 association rules with a confidence value of more than 0.9 explored by the APRIORI algorithm and after the clinical expert feedback, 10 clinically meaningful relations have been detected and reported. Due to the high number of risk factors, the use of data mining is effective for cancer data. These patterns provide the future study hypotheses of specific clinical studies.