RESUMO
Introduction: Breast cancer is one of the most common cancers affecting women. In mammography, differentiating a malignant tumor from a benign one is a very tedious task due to their structural similarities. Machine Learning [ML] is a subfield of Artificial Intelligence that can be used as an effective tool to help physicians to make decisions. Support vector machine [SVM] is one of the most common ML techniques that its performance depends on kernel parameters tuning and input features. The aim of this study was to investigate the effect of feature selection and different kernel functions on SVM performance
Methods: This analytic study was performed through comparative method. Genetic algorithm was used for feature selection. SVM models based on different kernel functions, including polynomial, Linear, Radial Basis Function [RBF], Quadratic and Multi-Layer Perceptron [MLP], were first performed with all features and then, with the selected features. The Wisconsin original breast cancer data set was used as a training set to evaluate the performance of the classifiers. All implementations were done in MATLAB environment
Results: According to the obtained results, by applying feature selection, the performance of SVM with MLP kernel function decreased and with quadratic kernel function increased. However, the performances of the linear and RBF kernels were desirable in both conditions. Generally, after the dimension reduction, the best accuracy, specificity, sensitivity and accuracy were dropped by 0.663, 0.833, 1.077 and 0.138 percent respectively
Conclusion: The ML-based methods can help physicians in diagnosis and decision makings for treatment
RESUMO
OBJECTIVES: This study aimed to compare nurses' satisfaction with, and expectations of, hospital information systems in two teaching hospitals. METHODS: This was a survey study, which was completed in 2014. The potential participants were 267 nurses who worked in two teaching hospitals and used the same hospital information system. Data were collected using two questionnaires. Both questionnaires were examined in terms of content validity and reliability. RESULTS: The results showed that, for a majority of nurses, their expectations of the system were not met in either hospital. Moreover, there was a significant association between the nurses' expectations and the perceived usefulness of the systems (p < 0.001), between the nurses' expectations and their satisfaction with the systems (p < 0.001), and between the perceived usefulness and nurses' satisfaction with the systems (p < 0.001). CONCLUSIONS: The results suggested that, apart from the technical issues of implementing clinical information systems, non-technical factors should be taken into account. Among them, the nature of clinical tasks and the organizational culture require more attention to allow a successful system to be designed and implemented.