Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients
Remote monitoring of a patient’s vital activities has become increasingly important in dealing with various medical applications. In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG) signals in cardiac patients to classify heart health sta...
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2025-02-01
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| author | Sohaib R. Awad Faris S. Alghareb |
| author_facet | Sohaib R. Awad Faris S. Alghareb |
| author_sort | Sohaib R. Awad |
| collection | DOAJ |
| description | Remote monitoring of a patient’s vital activities has become increasingly important in dealing with various medical applications. In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG) signals in cardiac patients to classify heart health status. This trend is largely driven by the growing interest in computer-aided diagnosis based on ML algorithms. However, there has been inadequate investigation into the impact of risk factors on heart health, which hinders the ability to identify heart-related issues and predict the conditions of cardiac patients. In this context, developing a GUI-based classification approach can significantly facilitate online monitoring and provide real-time warnings by predicting potential complications. In this paper, a general framework structure for medical real-time monitoring systems is proposed for modeling the vital signs of cardiac patients in order to predict the patient’s status. The proposed approach analyzes AI-driven interventions to provide a more accurate cardiac diagnosis and real-time monitoring system. To further demonstrate the validity of the presented approach, we employ it in a LabVIEW-based remote tracking system to predict three healthcare statuses (stable, unstable non-critical, and unstable critical). The developed monitoring system receives various information about patients’ vital signs, and then it leverages a novel encoding-based machine learning algorithm to pre-process, analyze, and classify patient status. The developed ANN classifier and proposed encoding-based ML model are compared to other conventional ML-based models, such as Naive Bayes, SVM, and KNN for model accuracy evaluation. The obtained outcomes demonstrate the efficacy of the presented ANN and encoding-based ML approaches by achieving an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the developed ANN classifier and the proposed encoding-based technique, respectively, whereas Naive Bayes and quadratic SVM algorithms realize <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>94.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively. In short, this study aims to explore how ML algorithms can enhance diagnostic accuracy, improve real-time monitoring, and optimize treatment outcomes. Meanwhile, the proposed tracking system outperforms most existing monitoring systems by offering high classification accuracy of the heart health status and a user-friendly interactive interface. Therefore, it can potentially be utilized to improve the performance of remote healthcare monitoring for cardiac patients. |
| format | Article |
| id | doaj-art-034be74fe2474285b060521a1659bf89 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Algorithms |
| spelling | doaj-art-034be74fe2474285b060521a1659bf892025-08-20T03:11:19ZengMDPI AGAlgorithms1999-48932025-02-011829410.3390/a18020094Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac PatientsSohaib R. Awad0Faris S. Alghareb1Department of Computer and Informatics Engineering, College of Electronics, Ninevah University, Mosul 41002, IraqDepartment of Computer and Informatics Engineering, College of Electronics, Ninevah University, Mosul 41002, IraqRemote monitoring of a patient’s vital activities has become increasingly important in dealing with various medical applications. In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG) signals in cardiac patients to classify heart health status. This trend is largely driven by the growing interest in computer-aided diagnosis based on ML algorithms. However, there has been inadequate investigation into the impact of risk factors on heart health, which hinders the ability to identify heart-related issues and predict the conditions of cardiac patients. In this context, developing a GUI-based classification approach can significantly facilitate online monitoring and provide real-time warnings by predicting potential complications. In this paper, a general framework structure for medical real-time monitoring systems is proposed for modeling the vital signs of cardiac patients in order to predict the patient’s status. The proposed approach analyzes AI-driven interventions to provide a more accurate cardiac diagnosis and real-time monitoring system. To further demonstrate the validity of the presented approach, we employ it in a LabVIEW-based remote tracking system to predict three healthcare statuses (stable, unstable non-critical, and unstable critical). The developed monitoring system receives various information about patients’ vital signs, and then it leverages a novel encoding-based machine learning algorithm to pre-process, analyze, and classify patient status. The developed ANN classifier and proposed encoding-based ML model are compared to other conventional ML-based models, such as Naive Bayes, SVM, and KNN for model accuracy evaluation. The obtained outcomes demonstrate the efficacy of the presented ANN and encoding-based ML approaches by achieving an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the developed ANN classifier and the proposed encoding-based technique, respectively, whereas Naive Bayes and quadratic SVM algorithms realize <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>94.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively. In short, this study aims to explore how ML algorithms can enhance diagnostic accuracy, improve real-time monitoring, and optimize treatment outcomes. Meanwhile, the proposed tracking system outperforms most existing monitoring systems by offering high classification accuracy of the heart health status and a user-friendly interactive interface. Therefore, it can potentially be utilized to improve the performance of remote healthcare monitoring for cardiac patients.https://www.mdpi.com/1999-4893/18/2/94cardiac patientscomputer-aided diagnosisencodinghealthcaremachine learningmulti-class classification |
| spellingShingle | Sohaib R. Awad Faris S. Alghareb Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients Algorithms cardiac patients computer-aided diagnosis encoding healthcare machine learning multi-class classification |
| title | Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients |
| title_full | Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients |
| title_fullStr | Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients |
| title_full_unstemmed | Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients |
| title_short | Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients |
| title_sort | encoding based machine learning approach for health status classification and remote monitoring of cardiac patients |
| topic | cardiac patients computer-aided diagnosis encoding healthcare machine learning multi-class classification |
| url | https://www.mdpi.com/1999-4893/18/2/94 |
| work_keys_str_mv | AT sohaibrawad encodingbasedmachinelearningapproachforhealthstatusclassificationandremotemonitoringofcardiacpatients AT farissalghareb encodingbasedmachinelearningapproachforhealthstatusclassificationandremotemonitoringofcardiacpatients |