| dc.contributor.author | Roy, Souradip | |
| dc.description.abstract | Cancer is one of the dangerous diseases which causes many deaths each year and breast cancer being one of them which is quite common among women. In today’s time 12 percent of the women can develop breast cancer over her course of lifetime. There are two kinds of tumors that can be found in women, they are benign and malignant. The former is considered non-cancerous while the latter is deadly. In this work we applied different machine learning models and did a comparative study to see which one performs better in predicting unseen data to be benign or malignant. The dataset we have used is imbalanced, so we also experimented by improving the prediction of our models using oversampling technique on the minority class. We have calculated Accuracy, F1-scores, AUC and Confusion Matrix as our measures to evaluate and compare our models. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.title | Breast Cancer Diagnosis Using Different Machine Learning Techniques | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2019-11-15T22:35:34Z | |
| dc.date.available | 2019-11-15T22:35:34Z | |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/31336 | |
| dc.subject.lcsh | Breast -- Cancer -- Diagnosis. | |
| dc.subject.lcsh | Machine learning. | |
| dc.subject.lcsh | Data mining. | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Ludwig, Simone | |
