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RISS 인기검색어
- 검색키워드
- 저자 : Ahmed Fawzi Otoom,Emad E. Abdallah,Yousef Kilani,Ahmed Kefaye,Mohammad Ashour (검색결과 2 건)
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Effective Diagnosis and Monitoring of Heart Disease
Ahmed Fawzi Otoom,Emad E. Abdallah,Yousef Kilani,Ahmed Kefaye,Mohammad Ashour 보안공학연구지원센터 2015 International Journal of Software Engineering and Vol.9 No.1
Wearable sensor mobile technologies and machine learning techniques are considered as two of the key research areas in the computer science and healthcare application industries. Our main aim is to build a simple yet accurate mobile application that is capable of real-time diagnosis and monitoring of patients with Coronary Artery Disease (CAD) or heart disease which is a major cause of death worldwide. Most available mobile healthcare systems focus on the data acquisition and monitoring component with little attention paid to real-time diagnosis. In this work, we build an intelligent classifier that is capable of predicting a heart disease problem based on clinical data entered by the user or the doctor and by using machine learning algorithms. This diagnosis component is integrated in the mobile application with a real-time monitoring component that continuously monitors the patient and raises an alarm whenever an emergency occurs. Our results show that the proposed diagnosis component has proved successful with a classification performance accuracy of more than 85% with the cross-validation test. Moreover, the monitoring algorithm provided a 100% detection rate.
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Ahmed Fawzi Otoom,Emad E. Abdallah,Maen Hammad 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.2
Recently, there has been greater attention to the use of classifier systems in medical diagnosis. Medical diagnostic tools provide automated procedures for objective decisions by making use of quantitative measures and machine learning techniques. These tools are effective and helpful for medical experts to diagnose diseases. One of such diseases is breast cancer which is the second largest cause of cancer deaths among women. To build an intelligent tool, it is very important to have an effective set of features. Two types of feature sets have been commonly implemented for the purpose of breast cancer diagnosis: image shape-based features and microarray gene expression data. Both types of feature sets have been widely implemented; however, there has been no work that directly compared the classification performance of these two feature sets. In this paper, we intensively review related works that used both types of feature sets and we also review the implemented machine learning algorithms. Moreover, we run extensive experiments to compare the classification performance of the aforementioned feature sets. Our results show that the image shape-based features are more discriminative for breast cancer classification when tested with ten-fold cross validation. To check the robustness of the best performing feature set, we further examine it with five-fold cross validation and with a variety of generative classification algorithms.
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