Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning

• Published in: JMIR medical informatics Vol. 10; no. 2; p. e30483
• Main Authors: Pičulin, Matej, Smole, Tim, Žunkovič, Bojan, Kokalj, Enja, Robnik-Šikonja, Marko, Kukar, Matjaž, Fotiadis, Dimitrios I, Pezoulas, Vasileios C, Tachos, Nikolaos S, Barlocco, Fausto, Mazzarotto, Francesco, Popović, Dejana, Maier, Lars S, Velicki, Lazar, Olivotto, Iacopo, MacGowan, Guy A, Jakovljević, Djordje G, Filipović, Nenad, Bosnić, Zoran
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 02.02.2022
• Subjects: Artificial intelligence; Cardiology; Cardiomyopathy; Chronic obstructive pulmonary disease; Decision making; Decision trees; Deep learning; Electrocardiography; Kidney diseases; Machine learning; Medical prognosis; Medicine; Methods; Multiple sclerosis; Original Paper; Parkinson's disease; Patients; Risk factors; Support vector machines; Young adults; cardiovascular disease; prediction model; AI; disease progression; sudden cardiac death; machine learning; artificial intelligence; SCD; prediction; hypertrophic cardiomyopathy; cardiomyopathy; validation; ML
• ISSN: 2291-9694; 2291-9694
• DOI: 10.2196/30483

Cardiovascular disorders in general are responsible for 30% of deaths worldwide. Among them, hypertrophic cardiomyopathy (HCM) is a genetic cardiac disease that is present in about 1 of 500 young adults and can cause sudden cardiac death (SCD). Although the current state-of-the-art methods model the risk of SCD for patients, to the best of our knowledge, no methods are available for modeling the patient's clinical status up to 10 years ahead. In this paper, we propose a novel machine learning (ML)-based tool for predicting disease progression for patients diagnosed with HCM in terms of adverse remodeling of the heart during a 10-year period. The method consisted of 6 predictive regression models that independently predict future values of 6 clinical characteristics: left atrial size, left atrial volume, left ventricular ejection fraction, New York Heart Association functional classification, left ventricular internal diastolic diameter, and left ventricular internal systolic diameter. We supplemented each prediction with the explanation that is generated using the Shapley additive explanation method. The final experiments showed that predictive error is lower on 5 of the 6 constructed models in comparison to experts (on average, by 0.34) or a consortium of experts (on average, by 0.22). The experiments revealed that semisupervised learning and the artificial data from virtual patients help improve predictive accuracies. The best-performing random forest model improved R from 0.3 to 0.6. By engaging medical experts to provide interpretation and validation of the results, we determined the models' favorable performance compared to the performance of experts for 5 of 6 targets.