Risk Prediction-Based Breast Cancer Diagnosis Using Personal Health Records and Machine Learning Models

Abstract

Breast cancer is most common in middle-aged female population. It is the fourth most dangerous cancer compared to remaining cancers. In recent years, breast cancer patients are significantly increasing, so the early diagnosis of cancer has become a necessary task in the cancer research, to facilitate subsequent clinical management of patients. The prevention of the breast cancer tumor is early detection of the tumor. Early detection of cancer can stop increase in tumor and saves lives. In the field of machine learning classification, cancer patients are classified into two types as benign or malignant. Different preprocessing techniques like filling missing values, applying correlation coefficient, synthetic minority oversampling technique (SMOTE) and tenfold cross-validations are implemented and aptly used to obtain the accuracy. The main context of this study is to identify key features from the dataset and analyze the performance evaluation of different machine learning algorithms like random forest classifier, logistic regression, support vector machine, decision tree, Gaussian Naive Bayes and k-nearest neighbors. Based on the results, the classification model that gives highest accuracy will be used as the best model for cancer prediction.