Breast cancer has made its mark as the primary cause of female deaths and disability worldwide, making it a significant health problem. However, early diagnosis of breast cancer can lead to its effective treatment. The relevant diagnostic features available in the patient’s medical data may be used in an effective way to diagnose, categorize and classify breast cancer. Considering the importance of early detection of breast cancer in its effective treatment, it is important to accurately diagnose and classify breast cancer using diagnostic features present in available data. Automated techniques based on machine learning are an effective way to classify data for diagnosis. Various machine learning based automated techniques have been proposed by researches for early prediction/diagnosis of breast cancer. However, due to the inherent criticalities and the risks coupled with wrong diagnosis, there is a dire need that the accuracy of the predicted diagnosis must be improved. In this paper, we have introduced a novel supervised machine learning based approach that embodies Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network and Multilayer Perception methods. Experimental results show that the proposed framework has achieved an accuracy of 99.12%. Results obtained after the process of feature selection indicate that both preprocessing methods and feature selection increase the success of the classification system.
The control of bleeding is of paramount importance in the management cancer patients. This study was undertaken to explore the outcomes after hemostatic radiation therapy (HRT) in advanced stage malignancies presenting with bleeding. Materials/Methods: Patients treated by HRT between 2014 and 2015 were analyzed retrospectively after obtaining approval from the Institutional Review Board. The degree of bleeding was assessed per the World Health Organization (WHO) scale (grade 0 Z no bleeding, 1 Z petechial bleeding, 2 Z clinically significant bleeding, 3 Z bleeding requiring transfusion, 4 Z bleeding associated with fatality). Our primary endpoint was bleeding at the end of radiation therapy, while the secondary endpoint was acute toxicity. Comparison was made for the bleeding scale before and after HRT using the Wilcoxon signed rank test. Results: A total of 28 patients with advanced malignancies that presented with bleeding were analyzed. Median age was 59 years (range, 30 e 92 years). Before treatment with HRT, bleeding was recorded as grade 2 in 15 (53%) and grade 3 in 13 (47%) patients. A median dose of 20 Gy (range, 8e40 Gy) of HRT was used to stop the bleeding. At the end of HRT, the results were promising with a statistically significant difference in bleeding (p < 0.001). Post HRT bleeding score was recorded as grade 0 in 68% (n Z 19), grade 1 in 21% (n Z 6), grade 2 in 7% (n Z 2), grade 3 in 4% (n Z 1) and grade 4 in none (n Z 0). An improvement was also noted in the median hemoglobin, which improved from 9.05 g/dL pre-HRT to 10.0 g/dL post-HRT. The median follow-up in our study was 1 month (range, 1 e 5 months), since most of the patients were discharged after palliative radiation therapy. Toxicity profile was reasonable with no grade 3 or above acute toxicity being observed in the study. Conclusion: HRT appears to be a safe and effective treatment modality for securing hemostasis in clinically bleeding patients.
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.