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Advanced Machine Learning for Estimating Vascular Occlusion Percentage in Patients With Ischemic Heart Disease and Periodontitis Publisher



Yadalam PK1 ; Shenoy SB2 ; Anegundi RV1 ; Mosaddad SA3 ; Heboyan A4, 5
Authors

Source: International Journal of Cardiology: Cardiovascular Risk and Prevention Published:2024


Abstract

Objective: The study aimed to assess the efficacy of advanced machine learning algorithms in estimating the percentage of vascular occlusion in ischemic heart disease (IHD) cases with periodontitis. Methods: This study involved 300 IHD patients aged 45 to 65 with stage III periodontitis undergoing coronary angiograms. Dental and periodontal examinations assessed various factors. Coronary angiograms categorized patients into three groups based on artery stenosis. Clinical data were processed, outliers were identified, and machine learning algorithms were applied for analysis using the orange tool, including confusion matrices and receiver operating characteristic (ROC) curves for assessment. Results: The results showed that Random Forest, Naive Bayes, and Neural Networks were 97 %, 84 %, and 92 % accurate, respectively. Random Forest did exceptionally well in identifying the severity of conditions, with 95.70 % accuracy for mild cases, 84.80 % for moderate cases, and a perfect 100.00 % for severe cases. Conclusions: The current study, using Periodontal Inflammatory Surface Area (PISA) scores, revealed that the Random Forest model accurately predicted the percentage of vascular occlusion. © 2024 The Authors
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