Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer

Abstract

Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.

Successful Outcomes of Single‐Port Insufflation Endoscopic Breast‐Conserving Surgery for Breast Cancer

“…HER2 positivity rate, tumor size, and age were not independent risk factors for local recurrence and metastasis in this study. However, other studies have found that HER2 status, tumor size, and age are risk factors for recurrence [24,25]. This inconsistency is likely due to a small sample size and short follow-up period in our report.…”

Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study

“…Their results inspired the models developed in the current study, which integrate gene expression profiling data and artificial intelligence algorithms in improved diagnostic tools for CRC. In another recent study, accuracy in predicting breast cancer recurrence was compared among conventional and recently developed data mining algorithms [ 4 ]. According to the comparison results, the decision tree C5.0 algorithm may be the best tool for predicting breast cancer recurrence, particularly 3-year recurrence, in patients who are in distant recurrence stage or nonrecurrence stage.…”

Comparative Analysis of Data Mining Algorithms for Cancer Gene Expression Data

“…M.A.Fahami et al [12] clustered the colon cancer patients into 2 important categories and as a result they found out top 20 genes that are effective in both the categories. Alireza et al [13] applied novel and traditional data mining methods viz, linear vector quantization (LVQ) neural network (NN), multi-layer perceptron (MLP), Bayesian NN, Decision Tree (DT-C5.0), kernel principal component analysis with support vector machine (KPCA-SVM), and random forest (RF). The author clearly demonstrates the impact of machine learning technology on breast cancer recur classification.…”

“…It was found that all the three algorithms were performing well but kNN outperforms the other two by a difference of around 0.4 percent in accuracy. ROC [11], [14], [15], [18] 4 AUC [11], [13], [15], [18] 4 F-Measure [13], [14], [18], [19] 4…”