West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. Asian Pacific Journal of Cancer Prevention 1513-7368 19 7 2018 07 01 Diagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-Analysis 1747 1752 65369 10.22034/APJCP.2018.19.7.1747 EN Ricvan Dana Nindrea Doctoral Program, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta City, Indonesia. Department of Public Health, Faculty of Medicine, Universitas Andalas, Padang City, Indonesia. 0000-0002-1844-3323 Teguh Aryandono Department of Surgery, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta City, Indonesia. 0000-0002-1143-4125 Lutfan Lazuardi Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta City, Indonesia. Iwan Dwiprahasto Department of Pharmacology and Therapy, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta City, Indonesia. Journal Article 2018 05 08 Objective: The aim of this study was to determine the diagnostic accuracy of different machine learning algorithms<br />for breast cancer risk calculation. Methods: A meta-analysis was conducted of published research articles on diagnostic<br />test accuracy of different machine learning algorithms for breast cancer risk calculation published between January 2000<br />and May 2018 in the online article databases of PubMed, ProQuest and EBSCO. Paired forest plots were employed for<br />the analysis. Numerical values for sensitivity and specificity were obtained from false negative (FN), false positive (FP),<br />true negative (TN) and true positive (TP) rates, presented alongside graphical representations with boxes marking the<br />values and horizontal lines showing the confidence intervals (CIs). Summary receiver operating characteristic (SROC)<br />curves were applied to assess the performance of diagnostic tests. Data were processed using Review Manager 5.3<br />(RevMan 5.3). Results: A total of 1,879 articles were reviewed, of which 11 were selected for systematic review and<br />meta-analysis. Fve algorithms for machine learning able to predict breast cancer risk were identified: Super Vector<br />Machine (SVM); Artificial Neural Networks (ANN); Decision Tree (DT); Naive Bayes (NB); and K-Nearest Neighbor<br />(KNN). With the SVM, the Area Under Curve (AUC) from the SROC was > 90%, therefore classified into the excellent<br />category. Conclusion: The meta-analysis confirmed that the SVM algorithm is able to calculate breast cancer risk with<br />better accuracy value than other machine learning algorithms. https://journal.waocp.org/article_65369_3bb0837a34fec5a8fc794abdcbba7274.pdf