Disease Characterization from PPA Signals by using Artificial Intelligence

Abstract

Humans go through a lot of problems with their mind and body during their course of life, which may lead to severe external as well as internal diseases or discomforts. This discomfort is nothing but change in the heart-rate, blood flow, respiration rate etc. These changes create certain patterns, which are different from the normal patterns. This data of abnormal or normal patterns can be used in to make models which can help to find out diseases using techniques of Artificial Intelligence (AI). Machine Learning and Artificial Neural Network (ANN) are major parts of AI which are used to train the model and predict on the basis of given data. In this paper a machine learning algorithm called as Support Vector Machine (SVM) and ANN is used to automatically analyze the blood flow variation datasets and classify the patterns into three classes to detect Coronary Artery Disease (CAD) pattern, Diabetes pattern and Hypertension pattern. Satisfactory results are obtained using SVM classifier.