Particle Swarm Optimization Based Filtering and Heart Disease Detection using Support Vector Machine
In day to day life monitoring our healthcare is important part of every human being; this is possible due to portable smart device. The machine learning technology is used to for classify the data according two parameter such as cost parameter and gamma parameter. In ECG signal there are many disease such as Bundelbranch block, cardiomyopathy, Dysrhythmia, Healthycontrol, Heartfailure, Myocardiallnfarction. In this paper Support Vector Machine is used, to classify their heart disease. The finite impulse response filter and Particle swarm optimization filter is used to for noise removal purpose. The PSO based filter gives the optimum result as compare to traditional FIR Filter. The R-peak detection is found using ECG windowmax function. The SVM is a group of machine learning techniques that are used to classify linear and non-linear data set. This machine first train the data then test. The process of features extraction is used to reduce components dimensionality. Keywords - Support Vector Machine (SVM), Finite Impulse Response (FIR), Particle Swarm Optimization (PSO), Electrocardiogram (ECG).