TY - JOUR
T1 - Transforming Cardiac Care
T2 - Machine Learning in Heart Condition Prediction Using Phonocardiograms
AU - D’souza, Sandra
AU - Niranjan Reddy, S.
AU - Tarun, Saikonda Krishna
AU - Sohan, P.
AU - Aneesha Acharya, K.
N1 - Publisher Copyright:
© 2024, Iran University of Science and Technology. All rights reserved.
PY - 2024/12
Y1 - 2024/12
N2 - The incidence of heart-related illnesses is on the rise worldwide. Heart diseases are primarily caused by a multitude of parameters, including high blood pressure, diabetes, and excessive cholesterol, which are controlled by poor dietary and lifestyle choices. The growth in cardiovascular diseases (CVD) is mostly due to several other behaviors, such as smoking, drinking, and sleeplessness. In the research, machine learning-based prediction methods work on the audio recordings of heartbeats known as phonocardiograms (PCG) to develop an algorithm that differentiates a normal healthy heart from an abnormal heart based on the heart sounds. The data set consists of 831 normal and 260 abnormal data, and the duration of each sample is 5 seconds. Features extracted from the data are up-sampled and applied to the logistic regression and random forest classification models. The developed models record a classification accuracy of 71% for logistic regression and 94% for the random forest model. Further, artificial neural networks (ANN) and Deep learning networks have been trained to improve performance and demonstrated an accuracy of 94.5%.
AB - The incidence of heart-related illnesses is on the rise worldwide. Heart diseases are primarily caused by a multitude of parameters, including high blood pressure, diabetes, and excessive cholesterol, which are controlled by poor dietary and lifestyle choices. The growth in cardiovascular diseases (CVD) is mostly due to several other behaviors, such as smoking, drinking, and sleeplessness. In the research, machine learning-based prediction methods work on the audio recordings of heartbeats known as phonocardiograms (PCG) to develop an algorithm that differentiates a normal healthy heart from an abnormal heart based on the heart sounds. The data set consists of 831 normal and 260 abnormal data, and the duration of each sample is 5 seconds. Features extracted from the data are up-sampled and applied to the logistic regression and random forest classification models. The developed models record a classification accuracy of 71% for logistic regression and 94% for the random forest model. Further, artificial neural networks (ANN) and Deep learning networks have been trained to improve performance and demonstrated an accuracy of 94.5%.
UR - http://www.scopus.com/inward/record.url?scp=85212154395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85212154395&partnerID=8YFLogxK
U2 - 10.22068/IJEEE.20.4.3324
DO - 10.22068/IJEEE.20.4.3324
M3 - Article
AN - SCOPUS:85212154395
SN - 1735-2827
VL - 20
JO - Iranian Journal of Electrical and Electronic Engineering
JF - Iranian Journal of Electrical and Electronic Engineering
IS - 4
M1 - 3324
ER -