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Artificial Intelligence and Nanotechnology for Diagnosis of Heart Disease

Shaikh Abdul Hannan

Early identification is critical for effective prevention and treatment of heart disease, which is a substantial health care concern. Traditional and non-invasive methods are time-consuming, uncomfortable, costly, and unsuited for periodic examination or diagnosis. There are numerous Non-Invasive (NI) approaches for diagnosing CVD. Data obtained by NI methods is primarily of three forms: (i) information derived from clinical variables, lab tests, and signs and symptoms (ii) raw cardiovascular data (ECG and PCG); or (iii) cardiac images. Three unique ML (machine learning) frameworks may be constructed based on the three types of data. Non-coronary cardiovascular illness (no-CHD) and myocardial cardiovascular disease (CHD) test findings were 80.1% and 76.9% accurate, respectively. SVM (Support Vector Machines) and artificial neural networks (Artificial Neural Network) were found to perform better in the majority of studies across all platforms. Deep neural networks are a relatively recent artificial intelligence technology that is producing impressive results in categorizing heart sounds and cardiovascular imaging. The current work will aid in the automation of the detection of cardiovascular disease by offering recommendations and possibilities for fresh researchers in the field of machine learning.