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This work focuses on the issue of diseases diagnosis based on data classification approaches. We consider mainly the diagnosis of heart diseases, diabetes, hepatitis and fetal risks. To do so, we employ a modified version of the SVDD algorithm, endowed with efficient tools to manage the multi-classification problems. Some other conventional algorithms such as SVM and RBF are, likewise, used to take...
Healthcare in simplest form is all about diagnosis and prevention of disease or treatment of any injury by a medical practitioner. It plays an important role in providing quality life for the society. The concern is how to provide better service with less expensive therapeutically equivalent alternatives. Machine Learning techniques (ML) help in achieving this goal. Healthcare has various categories...
We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The...
Accurate assessment of patients' risk against a certain disease is pivotal to healthcare management and personalized medicine. Although a variety of risk prediction models have been proposed in the literature, these models are mostly single-task, i.e. they only predict the risk of one disease at a time. However, in practice, the risks of multiple related diseases are often studied together. By separately...
Remote telehealth monitoring has become a common practice in home health care in the United Sates. Further, there is growing evidence of the effectiveness of remote monitoring on patient outcomes. The Visiting Nurse of New York, the largest not-for-profit home health care agency in the country tested the impact of remote monitors on a sample of 132 patients compared to a matched control group (n=264)...
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