Why the study?
Healthcare and disease detection at an early stage is important for proper and optimum disease detection.
Does a system using Particle Swarm Optimization, Support Vector Machine, and a Fuzzy PID controller improve heart disease detection from ECG signals?
Does a system using Particle Swarm Optimization, Support Vector Machine, and a Fuzzy PID controller improve heart disease detection from ECG signals?
A computational approach combining Particle Swarm Optimization for ECG denoising and Support Vector Machine with a Fuzzy PID controller can be utilized for accurate heart disease classification.
May enhance ECG-based detection models; leaves open clinical validation and outcome impact.
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.
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Awati et al. (2021) studied this question.
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