Iranian Journal of Biomedical Engineering (IJBME)
نوع مقاله : مقاله کامل پژوهشی
نویسندگان
1 گروه مهندسی پزشکی، دانشکده مهندسی برق و کامپیوتر، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران
2 مرکز تحقیقات نارسایی قلب، پژوهشکده قلب و عروق، دانشگاه علوم پزشکی و خدمات بهداشتی درمانی اصفهان، اصفهان، ایران
کلیدواژهها
موضوعات
عنوان مقاله English
نویسندگان English
Heart failure (HF) is a cardiovascular disorder with a substantial mortality rate, which can be assessed using registry data. This work aims to predict 6-month mortality (6-MM) and 12-month mortality (12-MM) of HF patients using registry data from before hospitalization, during hospitalization, and discharge phase. The data comprise 3968 HF records sourced from Persian Registry Of cardioVascular diseasE (PROVE)/HF registry.
We proposed an ensemble model employing popular Convolutional Neural Networks (CNNs) to predict the patients’ survival status. However, the HF registry, being tabular data, isn’t suitable for leveraging the benefits of CNNs. To address this challenge, we introduced a novel data-to-image transformation using self-organizing maps (SOMs) to convert HF data samples into images. We compared our proposed model with an existing data-to-image transformation method, as well as with multiple baseline classifiers that utilize the HF tabular data along with down-sampling.
The proposed model, using SOM for data-to-image transformation, outperformed all others, achieving the highest area under receiver operating characteristic (ROC) curve for both 6-MM and 12-MM predictions, with rates of 74.22% and 75.2%, respectively.
In conclusion, the proposed model, along with SOM for data-to-image transformation, effectively predicts the survival status of HF patients utilizing registry data.
کلیدواژهها English
