Dimensionality Reduction and Comparison of Classification Models for Breast Cancer Prognosis - Garg, R; Mongia, V (International Journal of Computer Sciences and Engineering, 2025)
Cancer is a most prevailing problem in the society now days. Generally cancer specifically Breast cancer is a major problem in women. On among three cases of cancer is a Breast cancer. There are many factors that affect the cancer. All these factors and the symptoms in the patient can be recorded using hardware and software. Now days, due to advancement in technology data of patient is recorded and processed by using analytical method. Data mining provides various methods to process this data effectively and efficiently. This processed data can be proven very useful in earlier detection of diseases. The earlier detection of these symptoms can be proven helpful to save life of a patient. In our research, original data on Breast cancer from Winconsin has been taken. This data set has 10 attribute and 699 instances. In this study, a comparative model has been developed that compare performance of various data mining technique on the dataset. The study reveals that BayesNet is the best classifier that correctly predicts cancer survivability in the patient. Further, KStar is the fastest algorithm that takes lowest computation time for the classification. In the next step dimensionality reduction using gain ratio is performed to find out most dominant factors causing Breast cancer.