Abstract
Healthcare and disease detection in early stage is important in every human being. Proper and optimum detection of disease with smart controller is done using Particle swarm optimization (PSO) and Support Vector Machine (SVM). The research includes the Fuzzy Proportional Integral and Derivative (Fuzzy PID) controller was used with support vector machine to classify the heart disease. Particle Swarm Optimization is designed to remove the noise introduced in Electrocardiogram signal. Fuzzy PID controller was implemented for disease detection and prediction. Fuzzy PID controller provides most accurate and stable results.
Highlights
The Electrocardiogram (ECG/EKG) signal represented with respect to time and frequency are considered for detection for different heart disease
Particle Swarm Optimization is used for noise removal from ECG signal and Fuzzy PID controller was implemented for proper disease detection and prediction
4.2 Particle swarm optimization (PSO) Noise Removal The PSO optimization is applied on noisy ECG signal to remove noise and get clean ECG signal
Summary
The Electrocardiogram (ECG/EKG) signal represented with respect to time and frequency are considered for detection for different heart disease. ECG signals are further evaluated by fuzzy PID controller for proper evaluation. Prosperity watching is by and by ending up being a bit of standard everyday presence. The current human administrations industry expects to give better prosperity organizations to people in a capable and patient sincere way. The recorded signals are taking from each cathoderay electrode This recorded sign is printed which is electrocardiogram. Proper and optimum detection of heart disease with PID controller is done using support vector machine and particle swarm optimization. Particle Swarm Optimization is used for noise removal from ECG signal and Fuzzy PID controller was implemented for proper disease detection and prediction. Fuzzy PID controller was used to provide most accurate and stable results
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