ECG Analysis-Based Cardiac Disease Prediction Using Signal Feature Selection with Extraction Based on AI Techniques

ECG (Electrocardiogram) performs classification using a machine learning model for processing different features in the signal. The electrical activity of heart is computed with signal library. key issue handling signals an estimation irregularities to evaluate health status patients. impulse waveform specialized tissues cardiac diseases. However, comprises difficulties associated derive certain features. Through (ML) input are signals. In this paper, proposed Noise QRS Feature effective classification. computes sequences. Initially, pre-processed Finite Impulse response (FIR) filter analysis processed and responses kNN performance comparatively examined Discrete Wavelet Transform (DWT), Dual-Tree Complex Transforms (DTCWT) Orthonormal Stockwell (DOST) Cascade Feed Forward Neural Network (CFNN), (FFNN). Simulation expressed that exhibits higher accuracy 99% which ~6 – 7% than conventional classifier model.