Background: Improper patient navigation and follow-up measures hamper breast cancer screening programs. To augment existing programs, we aimed to develop a decision support system for early breast cancer detection, by training and validating machine learning classification algorithms on routinely available patient data.Methods: Data were collected prospectively from eligible consenting women who visited a single university affiliated center in Tehran, Iran, during a two-year period. We selected 17 features from patient demographics, history, clinical examination and screening imaging. Breast cancer diagnosis was assessed one year after initial data collection. Positive outcomes where confirmed with tissue biopsy. Six supervised machine learning classification algorithms (including two artificial neural networks) were trained on 743 cases. Odds ratios were calculated using logistic regression.Results: 34% of participants were diagnosed with breast cancer. Highest adjusted odds ratios (95%CI) belonged to ultrasound: 24.8 (12.4,52.0) and mammography: 21.7 (8.8,58.5). When evaluated on all patients, random forest model possessed the highest AUC (95%CI) of 0.98 (0.97,0.99). The results of 10-fold stratified cross-validation supported model stability. Based on the mean of ten validation iterations, random forest provided the highest accuracy (93.3%) sensitivity (91.9%) and NPV (96.2%). K-nearest-neighbors model provided the highest specificity (95.9%) and PPV (91.9%).Conclusions: Machine learning models trained on basic demographics, history, clinical examination and breast screening imaging can predict breast cancer accurately. Such decision support tools when added to existing programs can boost the effectiveness of screening measures. Implementation ultimately depends on future works which will focus on external validation, interface development and cost-effectiveness analysis.
Background: COVID-19 mortality rates differ across countries. We aimed to construct a model that predicts mortality worldwide, by including only country-level socioeconomic and health system indicators and excluding variables related to short-term measures for pandemic management. Methods: COVID-19 mortality data was collected from Johns Hopkins University resource center. Additional sources were public reports from the United Nations, the World Bank and the Heritage Foundation. We implemented multiple linear regression with backward elimination on the selected predictors. Results: The final model constructed on seven Independent variables, significantly predicted COVID-19 mortality rate by country (F-statistic: 29.2, p<0.001). Regression coefficients (95% CI) in descending order of standardized effects: Annual tourist arrivals: 5.43 (4.03, 6.83); health expenditure per capita: 4.43 (2.92, 5.96); GDP (PPP): -4.60 (-6.81, -2.38); specialist surgical workforce per 100000: 2.63 (0.67, 4.59); number of physicians per 1000: -2.32 (-4.3, -0.28); economic freedom score: -1.35 (-2.60, -0.10); and total population: 1.66 (-0.19, 3.52). All VIF values were below 5, showing acceptable collinearity. R-squared (52.65%), adjusted R-squared (50.25%) and predicted R-squared (42.33%) showed strong model fit. Conclusion: limited country-level socioeconomic and health system indicators can explain COVID-19 mortality worldwide; emphasizing the priority of attending to these fundamental structures when planning for pandemic preparedness.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.