ArticleInternational journal of cardiology. Cardiovascular risk and prevention2024
Advanced machine learning for estimating vascular occlusion percentage in patients with ischemic heart disease and periodontitis.
Article in International journal of cardiology. Cardiovascular risk and prevention, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An interdisciplinary framework for artificial intelligence, precision medicine, and ethical governance in periodontal care: a systematic review.BMC oral health · 2026Pooled it
- Intravascular ultrasound wall shear stress imaging in stented coronary arteries with ultrafast Doppler.Scientific reports · 2026Article
- Morphotype stratification of radix entomolaris in mandibular molars.Journal of oral biology and craniofacial researchArticle
- Comparing gradient boosting and neural networks in the prediction of intersecting genes in gingival epithelial immunity.Journal of Indian Society of PeriodontologyArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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, Naïve 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.
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Registered trials
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