%0 Journal Article %T Diagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-Analysis %J Asian Pacific Journal of Cancer Prevention %I West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. %Z 1513-7368 %A Nindrea, Ricvan Dana %A Aryandono, Teguh %A Lazuardi, Lutfan %A Dwiprahasto, Iwan %D 2018 %\ 07/01/2018 %V 19 %N 7 %P 1747-1752 %! Diagnostic Accuracy of Different Machine Learning Algorithms for Breast Cancer Risk Calculation: a Meta-Analysis %K Breast cancer risk %K calculation %K Machine Learning %K algorithms %R 10.22034/APJCP.2018.19.7.1747 %X Objective: The aim of this study was to determine the diagnostic accuracy of different machine learning algorithmsfor breast cancer risk calculation. Methods: A meta-analysis was conducted of published research articles on diagnostictest accuracy of different machine learning algorithms for breast cancer risk calculation published between January 2000and May 2018 in the online article databases of PubMed, ProQuest and EBSCO. Paired forest plots were employed forthe analysis. Numerical values for sensitivity and specificity were obtained from false negative (FN), false positive (FP),true negative (TN) and true positive (TP) rates, presented alongside graphical representations with boxes marking thevalues and horizontal lines showing the confidence intervals (CIs). Summary receiver operating characteristic (SROC)curves were applied to assess the performance of diagnostic tests. Data were processed using Review Manager 5.3(RevMan 5.3). Results: A total of 1,879 articles were reviewed, of which 11 were selected for systematic review andmeta-analysis. Fve algorithms for machine learning able to predict breast cancer risk were identified: Super VectorMachine (SVM); Artificial Neural Networks (ANN); Decision Tree (DT); Naive Bayes (NB); and K-Nearest Neighbor(KNN). With the SVM, the Area Under Curve (AUC) from the SROC was > 90%, therefore classified into the excellentcategory. Conclusion: The meta-analysis confirmed that the SVM algorithm is able to calculate breast cancer risk withbetter accuracy value than other machine learning algorithms. %U https://journal.waocp.org/article_65369_3bb0837a34fec5a8fc794abdcbba7274.pdf