Browsing by Author "Lima, Ana Sofia"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Machine learning in tumor classification in breast cancerPublication . Lima, Ana Sofia; Coutinho, Carolina; Machado, Raquel; Oliveira, Alexandra Alves; Faria, Brígida Mónica; Faria, Brigida Monica; Oliveira, AlexandraBreast cancer is the primary cause of mortality among women worldwide (1). Discernible patterns can be found within the disease, presenting an opportunity for the application of machine learning (ML), garnering effective results in screening and diagnosis. Different ML algorithms were tested - Decision Tree, Deep Learning (DL), k-Nearest Neighbors (k-NN) and Naïve Bayes - to construct a predictive model allowing the early classification of a breast tumor as benign or malignant, avoiding the need to proceed to a more invasive technique. The ML models were constructed and applied to a database of 201 individuals with breast cancer and descriptive attributes (e.g. age, tumor size, presence of invasive nodes) (2) by using RapidMiner Studio. The evaluation of the models was done by analyzing their accuracy, true negative (TNR) and true positive rates (TPR), their ROC (Receiver Operating Characteristic) curves and AUC (Area Under Curve). During a first exploratory phase, fours clusters were detected: smaller tumor sizes, younger patients, and a benign diagnosis; older age, bigger tumor sizes and a malignant diagnosis; and two more with the opposite characteristics. These characteristics were later found to be important factors in the construction of the Decision Tree. When comparing the models accuracy, the best model was Naïve Bayes (91.04%), followed by the Decision Tree (90.55%), DL (90.02%) and k-NN (86.32%). There is a statistically significant difference between the performances of every model (p<0.05) except between the DL and the Decision Tree models. Naïve Bayes presented the highest TPR (98.21%) while DL presented the highest TNR (83.15%). The Decision Tree model presented the highest AUC (0.976), followed by Naïve Bayes (0.961). The Decision Tree model best achieved our goal by having the highest AUC which denotes an exceptional sensitivity rate, surpassing Naïve Bayes while maintaining a similar accuracy and TNR.
- Sustainable practices in daily life and in the context of tourismPublication . Ferreira, Fernanda A.; Liberato, Dália; Lima, Ana Sofia; Fernandes, Paula OdeteThe concern and desire to follow sustainable practices is increasing, which makes tourists take this into account when choosing a tourism destination. The general objective of this paper is to analyze the di erence between tourists' sustainable behavior in their daily lives and in the context of tourism. To answer the objectives of this study, we opted for a quantitative methodological approach since this research aims to study population behavior, opinions, and values. The results suggest that although in their daily lives' individuals adopt sustainable practices, sometimes it doesn't happen in the context of tourism. It was veri ed that if individuals felt they had the resources to adopt sustainable practices in the context of tourism, they would more often do so. Also, word of mouth, the perceived image of the destination, and guest reviews positively in uence the intention to visit a destination that adopts sustainable practices. This study helps to recognize the sustainable practices most frequently adopted, those that do not present di erences in the two contexts, and nally, the tools considered most important in the decision to visit a sustainable tourism destination.