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 ...
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 data...
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...
Transfer learning using the upper architecture representing a standard convolutional neural network model trained on a large dataset (ImageNet) and the transferred knowledge frozen by the weights of the new model [17]. Other layers will be retrained with X-ray data to allow new classification. ...
Artificial intelligence in imaging of coronary artery disease: current applications and future perspective Article23 February 2022 Explore related subjects References Greenspan, H., van Ginneken, B. & Summers, R. M. Guest editorial deep learning in medical imaging: overview and future promise of an...
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...
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 ...
K Nearest Neighbor (KNN) algorithm is applied to find the test data's neighborhood data points while the base classifiers mentioned are failed to classify correctly in any layer. To test the proposed model's efficiency, we have used a realistic dataset (70,000 instances) collected from Kaggle...
Contrast this with one dataset, reporting on cardiovascular disease from the Kaggle collection where there are nearly seventy thousand patient records. However, this Kaggle dataset reports only a small number of parameters per patient record, values such as serum cholesterol level, diastolic and ...