IoT PCR for Pandemic Disease Detection and Its Spread Monitoring

2019 ◽  
Author(s):  
Hanliang Zhu ◽  
Pavel Podesva ◽  
Xiaocheng Liu ◽  
Haoqing Zhang ◽  
Tomas Teply ◽  
...  
Keyword(s):  
Disease Detection

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

Nanowire Sensors Characterization and Validation using Matlab Models for Disease Detection

2011 ◽  
Vol 6 (2) ◽  
pp. 55-62
Author(s):  
Ushaa Eswaran ◽  
◽  
M. Madhavi Latha ◽  
G. Madhusudhan Rao ◽  
◽  
...  
Keyword(s):  
Disease Detection ◽  
Nanowire Sensors

Crop Disease Detection in Image Processing Using MATLAB

2020 ◽  
Vol 24 (04) ◽  
pp. 2967-2973
Author(s):  
Archana P ◽  
Hari prabhu S ◽  
Mohammed safir A ◽  
Naveenraj K ◽  
Pravin kumar S
Keyword(s):  
Image Processing ◽  
Disease Detection ◽  
Crop Disease

Crop Disease Detection in Image Processing Using MATLAB

2020 ◽  
Vol 24 (04) ◽  
pp. 1698-1703
Author(s):  
Archana P ◽  
Hari prabhu S ◽  
Mohammed safir A ◽  
Naveenraj K ◽  
Pravin kumar S
Keyword(s):  
Image Processing ◽  
Disease Detection ◽  
Crop Disease

Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: an Emerging Approach for Disease Detection and Treatment Optimization

2020 ◽  
Vol 28 ◽  
Author(s):  
Valeria Visco ◽  
Germano Junior Ferruzzi ◽  
Federico Nicastro ◽  
Nicola Virtuoso ◽  
Albino Carrizzo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Failure ◽  
Atrial Fibrillation ◽  
Precision Medicine ◽  
Clinical Decision Making ◽  
Disease Detection ◽  
Physician Patient Relationship ◽  
Invasive Approach ◽  
Tailored Treatment ◽  
The Impact

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

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