Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis
Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD ri...
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MDPI AG
2024-09-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/10/957 |
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| author | Ioannis D. Apostolopoulos Nikolaos I. Papandrianos Dimitrios J. Apostolopoulos Elpiniki Papageorgiou |
| author_facet | Ioannis D. Apostolopoulos Nikolaos I. Papandrianos Dimitrios J. Apostolopoulos Elpiniki Papageorgiou |
| author_sort | Ioannis D. Apostolopoulos |
| collection | DOAJ |
| description | Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. It investigates the impact of incorporating human clinical judgments into the RF model’s predictive capabilities. We recruited 606 patients from the Department of Nuclear Medicine at the University Hospital of Patras, Greece, from 16 February 2018 to 28 February 2022. Clinical data inputs included age, sex, comprehensive cardiovascular history (including prior myocardial infarction and revascularisation), CAD predisposing factors (such as hypertension, dyslipidemia, smoking, diabetes, and peripheral arteriopathy), baseline ECG abnormalities, and symptomatic descriptions ranging from asymptomatic states to angina-like symptoms and dyspnea on exertion. The diagnostic accuracies of human evaluators and the RF model (when trained with datasets inclusive of human judges’ assessments) were comparable at 79% and 80.17%, respectively. However, the performance of the RF model notably declined to 73.76% when human clinical judgments were excluded from its training dataset. These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics. |
| format | Article |
| id | doaj-art-bf8ad99130dc4d82b6460dfe829f367a |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-bf8ad99130dc4d82b6460dfe829f367a2025-08-20T02:11:04ZengMDPI AGBioengineering2306-53542024-09-01111095710.3390/bioengineering11100957Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease DiagnosisIoannis D. Apostolopoulos0Nikolaos I. Papandrianos1Dimitrios J. Apostolopoulos2Elpiniki Papageorgiou3Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, GreeceDepartment of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, GreeceDepartment of Nuclear Medicine, University Hospital of Patras, 26504 Rio, GreeceDepartment of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, GreeceCoronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. It investigates the impact of incorporating human clinical judgments into the RF model’s predictive capabilities. We recruited 606 patients from the Department of Nuclear Medicine at the University Hospital of Patras, Greece, from 16 February 2018 to 28 February 2022. Clinical data inputs included age, sex, comprehensive cardiovascular history (including prior myocardial infarction and revascularisation), CAD predisposing factors (such as hypertension, dyslipidemia, smoking, diabetes, and peripheral arteriopathy), baseline ECG abnormalities, and symptomatic descriptions ranging from asymptomatic states to angina-like symptoms and dyspnea on exertion. The diagnostic accuracies of human evaluators and the RF model (when trained with datasets inclusive of human judges’ assessments) were comparable at 79% and 80.17%, respectively. However, the performance of the RF model notably declined to 73.76% when human clinical judgments were excluded from its training dataset. These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics.https://www.mdpi.com/2306-5354/11/10/957coronary artery diseasemachine learningrandom forestprobability calibration |
| spellingShingle | Ioannis D. Apostolopoulos Nikolaos I. Papandrianos Dimitrios J. Apostolopoulos Elpiniki Papageorgiou Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis Bioengineering coronary artery disease machine learning random forest probability calibration |
| title | Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis |
| title_full | Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis |
| title_fullStr | Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis |
| title_full_unstemmed | Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis |
| title_short | Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis |
| title_sort | between two worlds investigating the intersection of human expertise and machine learning in the case of coronary artery disease diagnosis |
| topic | coronary artery disease machine learning random forest probability calibration |
| url | https://www.mdpi.com/2306-5354/11/10/957 |
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