15 research outputs found
Implementing decision tree-based algorithms in medical diagnostic decision support systems
As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems.
Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks.
We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models
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Breast cancer, medical imaging, and cancer genetics. A new genetic concept regarding the causes and prevention strategies of cancer is presented
Breast cancer is the most common cancer type in the United Kingdom. Many women with breast cancer do not show any noticeable symptoms in their early stages, hence regular breast screening is important. In this research focus is on medical imaging and its role in breast cancer screening, diagnosis, and treatment monitoring. Around 10% of all cancers are caused by inherited gene mutations which may cause cancer to run in families. Though, majority of cancer cases (up to 90%) are caused by acquired gene mutations which may also appear to run in families when family members share a particular environment or exposure. Genetic testing is conducted in this research on a number of participants to investigate the cancer cases found among their families. The findings of this research show that significant improvements have taken place in the emergence of hybrid imaging modalities used for breast imaging, through the fusion of different imaging techniques. The findings also provide evidence that similar to cancers caused by inherited gene mutations, cancers caused by non-inherited gene mutations may also appear to run in families when family members share certain environments and exposures or lifestyle behaviours. As a result, a new genetic concept of cancer essential to understand and control the disease is presented in this work which links between the human population origins and migrations, environmental factors and gene mutations, and the development of cancer. Furthermore, a number of cancer prevention strategies are recommended in this study to prevent people from getting the disease
2020 Student Symposium Research and Creative Activity Book of Abstracts
The UMaine Student Symposium (UMSS) is an annual event that celebrates undergraduate and graduate student research and creative work. Students from a variety of disciplines present their achievements with video presentations. It’s the ideal occasion for the community to see how UMaine students’ work impacts locally – and beyond.
The 2020 Student Symposium Research and Creative Activity Book of Abstracts includes a complete list of student presenters as well as abstracts related to their works
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Laboratory directed research and development. Annual report, fiscal year 1995
This document is a compilation of the several research and development programs having been performed at the Pacific Northwest National Laboratory for the fiscal year 1995