article · Heliyon
Cardiovascular diseases cause approximately 32 percent of deaths globally, making early prediction and accurate diagnosis essential to prevent clinical misinterpretation. This research explores machine learning systems to support early diagnosis by conducting a comparative analysis of established algorithms. Using the Cleveland and Statlog benchmark datasets, the study trained and tested decision trees, random forests, support vector machines, logistic regression, adaptive boosting, and K-nearest neighbours. The models were evaluated through ten-fold cross-validation with hyperparameter tuning, measuring accuracy, precision, recall, F1 score, and area under the curve metrics. The resulting comparative assessment demonstrates how machine learning algorithms can be optimised to identify signs of heart disease early. The findings suggest that such computational tools can assist medical professionals in making timely diagnoses, while offering analytical methodologies that could be applied to other health conditions.
Cardiovascular diseases represent nearly a third of all global deaths, and misinterpretation of medical tests remains a risk in everyday clinical practice. Providing doctors with dependable diagnostic tools driven by machine learning can assist in detecting heart conditions much earlier, potentially reducing diagnostic errors and helping healthcare practitioners intervene before serious complications occur.
The research operates at an early experimental stage, having evaluated algorithms on standard public datasets rather than in live clinical environments. The findings could inform clinical decision-support software for cardiologists and healthcare providers to assist in risk assessment. Moving towards real-world adoption would require further integration into hospital workflows and validation on diverse, contemporary patient populations.
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Heart disease is one of the most widespread global health issues, it is the reason behind around 32 % of deaths worldwide every year. The early prediction and diagnosis of heart diseases are critical for effective treatment and sickness management. Despite the efforts of healthcare professionals, cardiovascular surgeons and cardiologists' misdiagnosis and misinterpretation of test results may happen every day. This study addresses the growing global health challenge raised by Cardiovascular Diseases (CVDs), which account for 32 % of all deaths worldwide, according to the World Health Organization (WHO). With the progress of Machine Learning (ML) and Deep Learning (DL) techniques as part of Artificial Intelligence (AI), these technologies have become crucial for predicting and diagnosing CVDs. This research aims to develop an ML system for the early prediction of cardiovascular diseases by choosing one of the powerful existing ML algorithms after a deep comparative analysis of several. To achieve this work, the Cleveland and Statlog heart datasets from international platforms are used in this study to evaluate and validate the system's performance. The Cleveland dataset is categorized and used to train various ML algorithms, including decision tree, random forest, support vector machine, logistic regression, adaptive boosting, and K-nearest neighbors. The performance of each algorithm is assessed based on accuracy, precision, recall, F1 score, and the Area Under the Curve metrics. Hyperparameter tuning approaches have been employed to find the best hyperparameters that reflect the optimal performance of the used algorithms based on different evaluation approaches including 10-fold cross-validation with a 95 % confidence interval. The study's findings highlight the potential of ML in improving the early prediction and diagnosis of cardiovascular diseases. By comparing and analyzing the performance of the applied algorithms on both the Cleveland and Statlog heart datasets, this research contributes to the advancement of ML techniques in the medical field. The developed ML system offers a valuable tool for healthcare professionals in the early prediction and diagnosis of cardiovascular diseases, with implications for the prediction and diagnosis of other diseases as well.
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DOI: 10.1016/j.heliyon.2024.e38731
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