Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms
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 (...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2024-11-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3324-en.pdf |
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| author | Sandra D’Souza Niranjan Reddy S Saikonda Krishna Tarun Sohan P aneesha acharya k |
| author_facet | Sandra D’Souza Niranjan Reddy S Saikonda Krishna Tarun Sohan P aneesha acharya k |
| author_sort | Sandra D’Souza |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-a64649d0951e4402836adc528ec3f366 |
| institution | Kabale University |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-a64649d0951e4402836adc528ec3f3662025-01-09T18:47:15ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902024-11-012042332Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using PhonocardiogramsSandra D’Souza0Niranjan Reddy S1Saikonda Krishna Tarun2Sohan P3aneesha acharya k4 Dept. of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India-576104 Dept. of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India-576104 Dept. of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India-576104 Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India Dept. of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India-576104 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%.http://ijeee.iust.ac.ir/article-1-3324-en.pdfphonocardiogram (pcg)machine learninglogistic regressionrandom forestdeep learning |
| spellingShingle | Sandra D’Souza Niranjan Reddy S Saikonda Krishna Tarun Sohan P aneesha acharya k Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms Iranian Journal of Electrical and Electronic Engineering phonocardiogram (pcg) machine learning logistic regression random forest deep learning |
| title | Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms |
| title_full | Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms |
| title_fullStr | Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms |
| title_full_unstemmed | Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms |
| title_short | Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms |
| title_sort | transforming cardiac care machine learning in heart condition prediction using phonocardiograms |
| topic | phonocardiogram (pcg) machine learning logistic regression random forest deep learning |
| url | http://ijeee.iust.ac.ir/article-1-3324-en.pdf |
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