| Autor: |
S. Sobitha Ahila |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Deep Learning and Edge Computing Solutions for High Performance Computing ISBN: 9783030602642 |
| DOI: |
10.1007/978-3-030-60265-9_13 |
| Popis: |
Health informatics or healthcare informatics is a field of science that is evolving with the expansion of electronic health records (EHRs) and health analytics systems. At present, several applications are developed where the doctor is aided by the machine in detecting abnormalities to provide better healthcare. Here the focus is on EHRs and health analytics systems in detecting diabetic retinopathy and coronary heart disease identification. The ultimate aim is to identify the diverse forms of lesions during the early stages discovery of diabetic retinopathy. Search early detection could help to prevent permanent vision loss among diabetic patients. Image processing techniques that detect diabetic retinopathy lesions, aid in examining the image of the damaged part of the retina. The machine learning algorithm used employees’ specific color channels image features to separate physiological features from exudates digitally. The five-stage classification of the severity of the disease comprises of three stages of low risk and two stages of diabetic retinopathy. By implementing an automated system for identification of this disease we have a chance to accurately detect an affected patient easily. The second objective which is an intelligent system to detect coronary heart disease involves monitoring the heartbeat signals defined variations and monitoring patient’s health status automatically through sensor-dependent networks that employ internet of things technology. The primary aim here is that the patient is constantly monitored. This system relies on wireless sensors that are used the monitor cardiac patients using piezoelectric sensors that are utilized for measuring the arteries’ thickness and extract the waveforms without any intervention from humans. These collected waveforms can then be classified as normal or abnormal. This information is communicated to a mobile app immediately. The mobile plays the role of a display device and is also capable of uploading data to a cloud platform for detailed analysis. A cardiologist can then access the data and results from the cloud database. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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