Investigating the impact of nutrition and lifestyle on breast cancer: A data mining approach.

Breast cancer (BC) is the most common cancer and one of the main causes of death among women. This study was conducted to investigate the relationship between BC and nutrition and lifestyle, as well as compare machine learning models in predicting this disease. We designed a questionnaire related to nutrition and lifestyle with a nutritionist's guidance and provided them to 569 patients. After data gathering, we developed some machine-learning algorithms like logistic regression (LR), K-Nearest Neighbor (KNN), Decision tree (DT), and Support vector machine (SVM) classifiers. To make more accurate models, we used an oversampling method to avoid skewing the model due to the lack of balance in the target classes, a grid search method to adjust the model's hyperparameters and finally random forest to identify each variable's importance. The results of this research showed that the accuracy of the DT model was 0.95, SVM and LR were 0.93, and KNN was 0.86. The results indicated the better performance of DT among other models. Our findings show that it is possible to predict the type of cancerous tumor with relatively high accuracy without using specific information about the tumor itself. In particular, in our study, the decision tree has shown better accuracy compared to other models.