Objective: Predicting the ability of a breast cancer patient to survive was a difficult research problem for many scholars. Since the early dates of the relevant research, significant progress has been recorded in many related areas. For example, with innovative biomedical technologies, thanks to low-cost computer hardware and software, high-quality data is gathered and stored automatically, and lastly, with better analytical methods, that massive data is processed efficiently and effectively. Therefore, the objective of this document is to submit a report on a research project in which we have benefited from the technological developments available to develop predictive models of breast cancer and whether it exists or not. Methods and materials: artificial neural network, support vector machine, decision trees, naïve bayes, and random forest algorithms are used along with the most common statistical method (logistic regression) to build prediction models using a large data set. We also used the Holdout method. To avoid the unbalanced nature of the classes, the parameters of the performance evaluation are predefined.
Results:The results show that the Decision Tree (DT) is the top predictor with 89.1% accuracy on the holdout sample, surpassing all prediction accuracy reported in the literature; Artificial Neural Networks (ANN) came out to be the second with 88.9% accuracy; Naïve Bayes (NB) came out to be the third with 83.3% accuracy, Support Vector Machines (SVM) came out to be the fourth with 83.2% accuracy, and the Random Forest (RF) models came out to be the lowest of the five with 71.2% accuracy.
Conclusion:A comparative study of multiple predictive models for breast cancer survival using a large set of data and 5-fold cross-validation gave us an insight into the relative ability to predict different data extraction methods. After analyzing the data, we have reached this conclusion: the model is able to help those who need it by predicting whether they have breast cancer or not.
