article · Computational Intelligence and Neuroscience
A predictive machine learning approach was developed to assess cardiovascular disease risk using clinical characteristics. Utilising a dataset comprising approximately 70,000 patient records and 11 predictive features, the study evaluated several algorithms, including neural networks, random forests, Bayesian networks, C5.0, and QUEST. The resulting models achieved a high prediction accuracy of 99.1 per cent on training and testing data, exceeding the performance of earlier techniques. By identifying cardiovascular risks earlier, such models provide potential mechanisms for advance diagnosis and timely clinical intervention. The outcomes demonstrate that automated classification can assist in detecting heart conditions prior to critical failure, potentially guiding treatment strategies and helping individuals adjust lifestyle factors to enhance recovery and survival.
Cardiovascular diseases represent a leading cause of heart failure across the globe. Highly accurate risk prediction tools offer the potential to identify cardiac disease ahead of time. Detecting these risks early allows healthcare providers to implement timely interventions, supports patients in managing their conditions, and contributes to improved recovery and survival prospects.
This research could inform clinical decision-support software used by healthcare practitioners to detect cardiac disease risks prior to acute events. The technology is currently at an applied and tested stage using a retrospective dataset of 70,000 patient records, meaning translation to clinical practice would require validation within operational healthcare systems.
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Cardiovascular diseases (CVDs) are a common cause of heart failure globally. The need to explore possible ways to tackle the disease necessitated this study. The study designed a machine learning model for cardiovascular disease risk prediction in accordance with a dataset that contains 11 features which may be used to forecast the disease. 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, Bayesian networks, C5.0, and QUEST were compared for this dataset. On training and testing data sets, the results acquired a high accuracy (99.1 percent), which is significantly superior to previous methods. Ahead‐of‐time detection and diagnosis of cardiac disease, as well as better treatment outcomes, are strong possibilities for the suggested prediction model. Additionally, it may help patients better manage their illness or life forms in order to increase their chances of recovery/survival. The result showed greater accuracy and promising signs that machine‐learning algorithms can indeed assist in early identification of the disease and improvement of the treatment outcome.
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DOI: 10.1155/2023/9418666
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