Two of them are public heart disease datasets sourced from Kaggle and the third one is a local dataset collected from the medical records of patients at Dr.Heshmat Hospital, Guilan, Iran. The key contribution of this paper can be outlined as follows: Two public datasets besides a locally ...
This section tests the Cardiovascular Disease (CVD) classification output of the ICVD-ACOEDL algorithm using a dataset from the Kaggle repository20. There are 629 disease-affected samples and 561 normal samples in the collection. The information about the dataset is given in Table 3. It contains...
The dataset from Kaggle on cardiovascular disease includes approximately 70,000 patient records that were used to determine the outcome. Compared to the UCI dataset, the Kaggle dataset has many more training and validation records. Models created using neural networks, random forests, B...
(2) Hierarchical clustering on Cardiovascular Disease Prognostic datasets In the CVD prognostic dataset, Fig. 18 illustrates the normalized distances between rows and columns. The objective of normalization was to guarantee impartial treatment of individual attributes, and it was executed per column...
The dataset was derived from an ongoing cardiovascular study including inhabitants of Framingham, Massachusetts, and is freely accessible via the Kaggle website [34]. The classification is used to determine whether a patient has a 10-year chance of developing coronary heart disease (CHD). The data...
Kaggle: https://www.kaggle.com/datasets PyTorch: https://pytorch.org/ STACOM: http://stacom.cardiacatlas.org/ TensorFlow: https://www.tensorflow.org/ UK Biobank: https://www.ukbiobank.ac.uk/ Glossary Artificial intelligence (AI). In general, algorithms that mimic human intelligence; in this...
A. A primer on computational simulation in congenital heart disease for the clinician. Prog. Pediatr. Cardiol. 30, 3–13 (2010). Article Google Scholar Sermesant, M. et al. Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a ...
In contrast, our study addresses this limitation by utilizing Kaggle's cardiac dataset encompassing 70,000 patients and 11 features. The primary objective of this study is to minimize the risk of overfitting and accurately predict CVD by showcasing the effectiveness of using comprehensive datas...
The machine learning models are applied to a concrete dataset acquired from Kaggle. The models underwent training using a dataset that was partitioned into an 80:20 ratio. Machine learning model evaluation involves the utilization of performance measurements such as Accuracy, Precision, Recall, and ...
This system was tested on three datasets: The Cleveland dataset and the Heart Failure prediction dataset from Kaggle and heart disease UCI from Kaggle. We used various metrics to assess the system's efficiency, including recall, precision, f1-score, accuracy, and the ROC chart's area under ...