The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus
<i>Objective:</i> Early reports indicate that individuals with type 2 diabetes mellitus (T2DM) may have a greater incidence of breast malignancy than patients without T2DM. The aim of this study was to investigate the effectiveness of three different models for predicting risk of breast...
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MDPI AG
2019-11-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/11/11/1751 |
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| author | Meng-Hsuen Hsieh Li-Min Sun Cheng-Li Lin Meng-Ju Hsieh Chung Y. Hsu Chia-Hung Kao |
| author_facet | Meng-Hsuen Hsieh Li-Min Sun Cheng-Li Lin Meng-Ju Hsieh Chung Y. Hsu Chia-Hung Kao |
| author_sort | Meng-Hsuen Hsieh |
| collection | DOAJ |
| description | <i>Objective:</i> Early reports indicate that individuals with type 2 diabetes mellitus (T2DM) may have a greater incidence of breast malignancy than patients without T2DM. The aim of this study was to investigate the effectiveness of three different models for predicting risk of breast cancer in patients with T2DM of different characteristics. <i>Study design and methodology:</i> From 2000 to 2012, data on 636,111 newly diagnosed female T2DM patients were available in the Taiwan’s National Health Insurance Research Database. By applying their data, a risk prediction model of breast cancer in patients with T2DM was created. We also collected data on potential predictors of breast cancer so that adjustments for their effect could be made in the analysis. Synthetic Minority Oversampling Technology (SMOTE) was utilized to increase data for small population samples. Each datum was randomly assigned based on a ratio of about 39:1 into the training and test sets. Logistic Regression (LR), Artificial Neural Network (ANN) and Random Forest (RF) models were determined using recall, accuracy, F<sub>1</sub> score and area under the receiver operating characteristic curve (AUC). <i>Results:</i> The AUC of the LR (0.834), ANN (0.865), and RF (0.959) models were found. The largest AUC among the three models was seen in the RF model. <i>Conclusions:</i> Although the LR, ANN, and RF models all showed high accuracy predicting the risk of breast cancer in Taiwanese with T2DM, the RF model performed best. |
| first_indexed | 2024-03-12T07:38:49Z |
| format | Article |
| id | doaj.art-deb747b3d9f648dcabe9c2f7dd93afe9 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-12T07:38:49Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-deb747b3d9f648dcabe9c2f7dd93afe92023-09-02T21:26:37ZengMDPI AGCancers2072-66942019-11-011111175110.3390/cancers11111751cancers11111751The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes MellitusMeng-Hsuen Hsieh0Li-Min Sun1Cheng-Li Lin2Meng-Ju Hsieh3Chung Y. Hsu4Chia-Hung Kao5Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USADepartment of Radiation Oncology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 813, TaiwanManagement Office for Health Data, China Medical University Hospital, Taichung 404, TaiwanDepartment of Medicine, Poznan University of Medical Sciences, 60965 Poznan, PolandGraduate Institute of Biomedical Sciences, China Medical University, Taichung 404, TaiwanGraduate Institute of Biomedical Sciences, China Medical University, Taichung 404, Taiwan<i>Objective:</i> Early reports indicate that individuals with type 2 diabetes mellitus (T2DM) may have a greater incidence of breast malignancy than patients without T2DM. The aim of this study was to investigate the effectiveness of three different models for predicting risk of breast cancer in patients with T2DM of different characteristics. <i>Study design and methodology:</i> From 2000 to 2012, data on 636,111 newly diagnosed female T2DM patients were available in the Taiwan’s National Health Insurance Research Database. By applying their data, a risk prediction model of breast cancer in patients with T2DM was created. We also collected data on potential predictors of breast cancer so that adjustments for their effect could be made in the analysis. Synthetic Minority Oversampling Technology (SMOTE) was utilized to increase data for small population samples. Each datum was randomly assigned based on a ratio of about 39:1 into the training and test sets. Logistic Regression (LR), Artificial Neural Network (ANN) and Random Forest (RF) models were determined using recall, accuracy, F<sub>1</sub> score and area under the receiver operating characteristic curve (AUC). <i>Results:</i> The AUC of the LR (0.834), ANN (0.865), and RF (0.959) models were found. The largest AUC among the three models was seen in the RF model. <i>Conclusions:</i> Although the LR, ANN, and RF models all showed high accuracy predicting the risk of breast cancer in Taiwanese with T2DM, the RF model performed best.https://www.mdpi.com/2072-6694/11/11/1751type ii diabetes mellitusbreast cancerartificial neural networklogistic regressionrandom forest |
| spellingShingle | Meng-Hsuen Hsieh Li-Min Sun Cheng-Li Lin Meng-Ju Hsieh Chung Y. Hsu Chia-Hung Kao The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus Cancers type ii diabetes mellitus breast cancer artificial neural network logistic regression random forest |
| title | The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus |
| title_full | The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus |
| title_fullStr | The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus |
| title_full_unstemmed | The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus |
| title_short | The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus |
| title_sort | performance of different artificial intelligence models in predicting breast cancer among individuals having type 2 diabetes mellitus |
| topic | type ii diabetes mellitus breast cancer artificial neural network logistic regression random forest |
| url | https://www.mdpi.com/2072-6694/11/11/1751 |
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