Key result
Multiple machine learning models undergo evaluation for predicting cardiovascular diseases.
Why the study?
Early detection and accurate prediction of cardiovascular diseases are crucial, driving the need to evaluate machine learning algorithms to analyze complex medical datasets.
This study explores various machine learning algorithms for the prediction of cardiovascular diseases.
ML models may enhance CVD prediction; leaves open need for prospective clinical validation.
Cardiovascular diseases have emerged as a significant global health concern, affecting a large portion of the population. Early detection and accurate prediction of such diseases are crucial for providing dedicated care and minimizing the associated risks. To address this need, researchers have turned to machine learning algorithms and tools to analyze vast and complex datasets in the medical domain. In recent years, there has been a surge in the utilization of various machine learning techniques to assist in the intricate task of identifying heart-related issues.This study aims to explore and evaluate different models based on machine learning algorithms, namely Random Forest, KNN, Extra tree classifier, and XG boost, for the prediction of cardiovascular diseases. These models leverage the power of machine learning to analyze intricate patterns within the datasets, enabling the identification of potential risk factors and accurate disease prediction.
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Sinha et al. (2023) studied Cardiovascular diseases. Machine learning models (Random Forest, KNN, Extra tree classifier, XG boost) was evaluated on Prediction of cardiovascular diseases. Machine learning models including Random Forest, KNN, Extra tree classifier, and XG boost were explored and evaluated for the prediction of cardiovascular diseases.
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