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Diagnosis-of-Cardiovascular-Diseases

A proposed method for automated diagnosis of various diseases based on heart rate variability (HRV) analysis and machine learning. HRV analysis – consisting of time-domain analysis, frequency-domain analysis, and nonlinear analysis – is employed because its resulting parameters are unique for each disease and can be used as the statistical symptoms for each disease, while machine learning techniques are employed to automate the diagnosis process. The input data consist of electrocardiogram (ECG) recordings. The proposed method is divided into three main steps, namely dataset preparation step, machine learning step, and disease classification step. The dataset preparation step aims to prepare the training data for machine learning step from raw ECG signals, and to prepare the test data for disease classification step from raw RRI signals. The machine learning step aims to obtain the classifier model and its performance metric from the prepared dataset. The disease classification step aims to perform disease diagnosis from the prepared dataset and the classifier model. The implementation of data preparation step is subsequently described with satisfactory result.

nsansen/Binary-classifiers-for-noisy-datasets-a-comparative-study-of-existing-quantum-machine-learning-fra

nsansen

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